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May be elected as an interdepartmental major.

Effective Winter 2014

What is Informatics?

Informatics is the study of human and computer information processing systems from a socio-technical perspective. Michigan's unique interdisciplinary approach to this growing field of research and teaching emphasizes a solid grounding in contemporary computer programming, mathematics, and statistics, combined with study of the ethical and social science aspects of complex information systems. Experts in the field help design new information technology tools informed by scientific, business, and cultural contexts.

Informatics is where the technical accomplishments of computer science, mathematics, and statistics become embedded in the ways we interact, imagine, and produce in richer and more thoughtful ways. Students will obtain software development skills and learn a formal framework for making inferences from experimental and observational data, focusing on the manner and purpose in which people interact with information and information systems.

The major in Informatics is appropriate for students with varied interests and a range of background knowledge in information systems engineering, information analysis, and/or the use of information processing in biological, societal and emerging application areas. Students who complete the major are equipped to participate fully in important emerging areas such as bioinformatics, information analysis, large-scale information management, and human-centered information systems design. In addition, depending on which track a student selects, he or she develops the intellectual skills

  • to analyze enormous quantities of information (Information Analysis Track); 
  • to apply information technology to large-scale, cutting-edge problems in the life sciences (Life Science Informatics Track).

Students concentrating in Informatics have many opportunities available to them after graduation. The major provides excellent preparation for jobs in the IT industry as product managers, human factors engineers, usability specialists, information analysts in sciences and science related industries, and designers working with large software development teams. Recruiters visiting the university frequently are seeking students with the ideals and skill sets that are provided by this program. Combined with work in specific knowledge domains, from nursing to economics, graduates of Michigan's Informatics major are vital in leading organizations to harness emerging technologies. The deep understanding of the connections between information technology, data analysis, and organizations and society is also excellent background for students seeking to enter law school, business school, medical school, or schools of public policy. And, depending on the track they complete, students are well prepared for graduate study in many fields, including statistics, computer science, information, law, medicine, public health, and natural and social sciences.

 

Summary of Course Requirements and Prerequisites

The major in Informatics requires 40 credit hours for completion, including four core courses, 3-4 courses in one of two flexible tracks, plus electives selected from a list of recommended courses.

Four prerequisite courses serve as an introduction to core academic aspects of the curriculum and are required of all concentators. The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Core courses may be taken in any order and are required for completion of the major. Students may enroll in track courses before they have completed the entire core curriculum.

In pursuing the major in Informatics, students have the flexibility to specialize in one of two tracks:Information Analysis or Life Science Informatics. Each of the  tracks requires three to four courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the track.

In addition to the major's core and track requirements, students select major electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Committee for the major will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

  1. Data Mining & Information Analysis Track
    The collection, analysis, and visualization of complex data play critical roles in research, business, and government. Powerful tools from applied statistics, mathematics, and computational science can be used to uncover the meaning behind complex data sets. The Data Mining and Information Analysis track integrates these disciplines to provide students with practical skills and a theoretical basis for approaching challenging data analysis problems. Students in this track learn how to develop and test models for making predictions, to search through large collections of data for rare and unexpected patterns, and to characterize the degree of certainty associated with discoveries made in the course of data analysis. Skills and knowledge acquired in this track are increasingly important in the job market and are highly relevant for a number of graduate school programs.
  2.  Life Science Informatics Track
    Using artificial information systems, scientists have made great progress in identifying core components of organisms and ecosystems and are beginning to better understand how these components behave and interact with each other. In fact, biology has become an information science, as computational techniques have become an important means to develop and evaluate biological hypotheses. Informatics is used from basic biological research-studying how patterns of gene expression differ across various cell types-to the practice of medicine, where informatics is used to compare treatments, to identify social correlates of health, and to evaluate possible changes in health policy. The Life Science Informatics track prepares students for careers and advanced study in a number of information-related fields in the life sciences, as well as medical school and other areas of graduate study.

Field of Major and GPA calculation

For purposes of calculating grade point average, the term "field of the major" means the following:

  1. All STATS courses.
  2. All courses used to meet requirements for the major.
  3. All mandatory major prerequisites.

Informatics majors may not use any STATS courses toward the Area Distribution requirement.

Note. It is not necessary to complete all prerequisite courses prior to declaring an Informatics major.  Minimum grade for all prerequisite courses is a C.

Prerequisites to Core Courses

  1. SI 110 / SOC 110 with a C or better;
  2. MATH 115 with a C or better;
  3. EECS 182 / SI 182 or EECS 183 with a C or better;
  4. STATS 250 with a C or better;

Prerequisite to Declaration

MATH 115, STATS 250, and EECS 182 or 183.

Requirements for the Major

A minimum of 12 courses and a minimum of 40 credits.

  1. Core: EECS 203, EECS 280, STATS 403
  2. Subplans: Completion of one of the following tracks:
    1. Data Mining & Information Analysis track :
      1. MATH 217
      2. STATS 406
      3. STATS 415
      4. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      5. Electives*: 8 credits must be elected at the 300-level or higher
    2. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses:
        • BIOLOGY 305
        • MCDB 310
      3. Two of the following Quantitative/Computational courses:
        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 12-14 credits; 4 credits must be elected at the 300-level or higher
  3. Electives: Additional Informatics electives to bring total major credits to 40 credits (44 for Data Mining track). The number of electives required for each track varies, depending on the number of required core courses in the track. Informatics majors be allowed to select their electives from one of the following lists of courses, depending on their chosen track. Students who wish to use an elective that is not on this list should consult their track advisor before taking the course.  

 

Informatics Pre-Approved Electives

Students may chose electives for their declared track from the following pre-approved lists of electives without consultation of the track advisor.

Note: Only one elective course in a track indicated with "*" can be taken for elective credit.   

Course Data Mining & Information Analysis Life Science Informatics
BIOINF 463/MATH 463/BIOPHYS 463 Math Modeling in Biology  Data Mining & Information Analysis  Life Science Informatics 
BIOINF 527 Introduction to Bioinformatics & Computational Biology  Data Mining & Information Analysis *  
BIOINF 545/STATS 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data  Data Mining & Information Analysis * Life Science Informatics 
BIOINF 547/MATH 547/STATS 547 Probabilistic Modeling in Bioinformatics  Data Mining & Information Analysis  Life Science Informatics 
BIOINF 551/BIOLCHEM 551/CHEM 551/BIOMEDE 551/PATH 551 Proteome Informatics  Data Mining & Information Analysis * Life Science Informatics 
BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics  Data Mining & Information Analysis * Life Science Informatics 
BIOMEDE 551/BIOLCHEM 551/CHEM 551/BIOINF 551/PATH 551 Proteome Informatics  Data Mining & Information Analysis * Life Science Informatics 
BIOPHYS 463/MATH 463/BIOINF 463 Math Modeling in Biology  Data Mining & Information Analysis  Life Science Informatics 
BIOSTAT 449/STATS 449 Topics in Biostatistics  Data Mining & Information Analysis  Life Science Informatics 
BIOSTAT 646/BIOINF 545/STATS 545/Molecular Genetic and Epigenetic Data  Data Mining & Information Analysis * Life Science Informatics 
CHEM 551/BIOLCHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics  Data Mining & Information Analysis * Life Science Informatics 
CMPLXSYS 510/MATH 550 Introduction to Adaptive Systems  Data Mining & Information Analysis * Life Science Informatics 
EEB 485 Population and Community Ecology    Life Science Informatics *
EECS 281 Data Structures and Algorithms  Data Mining & Information Analysis  Life Science Informatics 
EECS 376 Foundations of Computer Science  Data Mining & Information Analysis  Life Science Informatics 
EECS 382 Internet‐scale Computing  Data Mining & Information Analysis  Life Science Informatics 
EECS 476 Theory of Internet Applications  Data Mining & Information Analysis  Life Science Informatics 
EECS 477 Introduction to Algorithms  Data Mining & Information Analysis  Life Science Informatics 
EECS 481 Software Engineering  Data Mining & Information Analysis  Life Science Informatics 
EECS 484 Database Management Systems  Data Mining & Information Analysis  Life Science Informatics 
EECS 485 Web Database and Information Systems  Data Mining & Information Analysis  Life Science Informatics 
EECS 487 Interactive Computer Graphics  Data Mining & Information Analysis  Life Science Informatics 
EECS 489 Computer Networks  Data Mining & Information Analysis  Life Science Informatics 
EECS 492 Introduction to Artificial Intelligence  Data Mining & Information Analysis  Life Science Informatics 
EECS 493 User Interface Development  Data Mining & Information Analysis  Life Science Informatics 
HONORS 352. Honors Introduction to Research in the Natural Sciences, section titled "Cyberscience" Data Mining & Information Analysis Life Science Informatics
IOE 510/MATH 561/OMS 518 Linear Programming I  Data Mining & Information Analysis *  
IOE 511/MATH 562 Continuous Optimization Methods  Data Mining & Information Analysis *  
IOE 512 Dynamic Programming  Data Mining & Information Analysis *  
MATH 416 Theory of Algorithms  Data Mining & Information Analysis  Life Science Informatics 
MATH 425/STATS 425 Introduction to Probability  Data Mining & Information Analysis  Life Science Informatics 
MATH 433 Introduction to Differential Geometry  Data Mining & Information Analysis   
MATH 451 Advanced Calculus I  Data Mining & Information Analysis  Life Science Informatics 
MATH 462 Mathematical Models  Data Mining & Information Analysis  Life Science Informatics 
MATH 463/BIOINF 463/BIOPHYS 463 Math Modeling in Biology  Data Mining & Information Analysis  Life Science Informatics 
MATH 471 Introduction to Numerical Methods  Data Mining & Information Analysis  Life Science Informatics 
MATH 525/STATS 525 Probability Theory  Data Mining & Information Analysis  Life Science Informatics 
MATH 526 Discrete State Stochastic Processes  Data Mining & Information Analysis  Life Science Informatics 
MATH 547/BIOINF 547/STATS 547 Probabilistic Modeling in Bioinformatics  Data Mining & Information Analysis  Life Science Informatics 
MATH 548/STATS 548 Computations in Probabilistic Modeling in Bioinformatics  Data Mining & Information Analysis  Life Science Informatics 
MATH 550/CMPLXSYS 510 Introduction to Adaptive Systems  Data Mining & Information Analysis * Life Science Informatics 
MATH 561/IOE 510/OMS 518 Linear Programming I  Data Mining & Information Analysis *  
MATH 562/IOE 511 Continuous Optimization Methods  Data Mining & Information Analysis *  
MATH 571 Numerical Methods for Scientific Computing I  Data Mining & Information Analysis   
MCDB 408 Genomic Biology  Data Mining & Information Analysis  Life Science Informatics 
MCDB 411 Protein Structure and Function    Life Science Informatics 
OMS 518/IOE 510/MATH 561 Linear Programming I  Data Mining & Information Analysis *  
PATH 551/BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551 Proteome Informatics  Data Mining & Information Analysis * Life Science Informatics 
SI 301 Models of Social Information Processing  Data Mining & Information Analysis * Life Science Informatics *
SI 422 Evaluation of Systems and Services  Data Mining & Information Analysis * Life Science Informatics *
SI 508 Networks: Theory and Application  Data Mining & Information Analysis   
SI 572 Database Design  Data Mining & Information Analysis * Life Science Informatics 
SI 583 Recommender Systems  Data Mining & Information Analysis *  
SI 631 Practical l Engagement Workshop: Content Management Systems  Data Mining & Information Analysis * Life Science Informatics *
SI 679 Aggregation and Prediction Markets  Data Mining & Information Analysis *  
SI 683 Reputation Systems  Data Mining & Information Analysis *  
SI 689 Computer Supported Cooperative Work  Data Mining & Information Analysis * Life Science Informatics *
STATS 401 Applied Statistical Methods II  Data Mining & Information Analysis  Life Science Informatics 
STATS 406 Introduction to Statistical Computing    Life Science Informatics 
STATS 408 Statistical Principles for Problem Solving: A Systems Approach  Data Mining & Information Analysis  Life Science Informatics 
STATS 415 Data Mining    Life Science Informatics 
STATS 425/MATH 425 Introduction to Probability  Data Mining & Information Analysis  Life Science Informatics 
STATS 426 Introduction to Theoretical Statistics  Data Mining & Information Analysis  Life Science Informatics 
STATS 430 Applied Probability  Data Mining & Information Analysis  Life Science Informatics 
STATS 449/BIOSTAT 449 Topics in Biostatistics  Data Mining & Information Analysis  Life Science Informatics 
STATS 470 Introduction to the Design of Experiments  Data Mining & Information Analysis  Life Science Informatics 
STATS 480 Survey Sampling Techniques  Data Mining & Information Analysis  Life Science Informatics 
STATS 500 Applied Statistics I  Data Mining & Information Analysis  Life Science Informatics 
STATS 525/MATH 525 Probability Theory  Data Mining & Information Analysis  Life Science Informatics 
STATS 526/MATH 526 Discrete State Stochastic Processes  Data Mining & Information Analysis  Life Science Informatics 
STATS 545/BIOINF 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data  Data Mining & Information Analysis * Life Science Informatics 
STATS 547/MATH 547/BIOINF 547 Probabilistic Modeling in Bioinformatics  Data Mining & Information Analysis  Life Science Informatics 
STATS 548/MATH 548 Computations in Probabilistic Modeling in Bioinformatics  Data Mining & Information Analysis  Life Science Informatics 

Honors Plan

Students interested in doing original research in informatics are encouraged to consider the Informatics Honors Plan. The Honors major is open to all Informatics majors who have achieved both a major GPA and an overall GPA of 3.4 or better. At least one year prior to graduation, interested students should identify a member of the U-M faculty with informatics expertise to serve as their faculty advisor.  Together with that person, the student prepares a 2-3 paragraph summary of the proposed thesis project, which is submitted together with the Honors Plan Application to the Informatics Program Coordinator for review by department advisors.  The student completes the thesis work in the senior year, while enrolling in 3-4 credits of independent study (such as EECS 499, MATH 399, SI 491, STATS 489, HONORS 390, or HONORS 490).  At least six weeks before the last day of classes in the term in which the student will complete the independent study and thesis, an electronic copy of the final Honors thesis is submitted to the Informatics program coordinator. The faculty advisor then solicits comments on the completed thesis from an independent reader, and the student presents the work in a public forum.

 

Informatics Major (Fall 2013) +

May be elected as an interdepartmental major.

Effective Fall 2013

What is Informatics?

Informatics is the study of human and computer information processing systems from a socio-technical perspective. Michigan's unique interdisciplinary approach to this growing field of research and teaching emphasizes a solid grounding in contemporary computer programming, mathematics, and statistics, combined with study of the ethical and social science aspects of complex information systems. Experts in the field help design new information technology tools informed by scientific, business, and cultural contexts.

Informatics is where the technical accomplishments of computer science, mathematics, and statistics become embedded in the ways we interact, imagine, and produce in richer and more thoughtful ways. Students will obtain software development skills and learn a formal framework for making inferences from experimental and observational data, focusing on the manner and purpose in which people interact with information and information systems.

The major in Informatics is appropriate for students with varied interests and a range of background knowledge in information systems engineering, information analysis, and/or the use of information processing in biological, societal and emerging application areas. Students who complete the major are equipped to participate fully in important emerging areas such as bioinformatics, information analysis, large-scale information management, and human-centered information systems design. In addition, depending on which track a student selects, he or she develops the intellectual skills

  • to analyze enormous quantities of information (Information Analysis Track); 
  • to reason systematically about the social impacts of and on information systems (Social Computing Track); 
  • to reason about the design of information systems (Computational Informatics Track); or 
  • to apply information technology to large-scale, cutting-edge problems in the life sciences (Life Science Informatics Track).

Students concentrating in Informatics have many opportunities available to them after graduation. The major provides excellent preparation for jobs in the IT industry as product managers, human factors engineers, usability specialists, information analysts in sciences and science related industries, and designers working with large software development teams. Recruiters visiting the university frequently are seeking students with the ideals and skill sets that are provided by this program. Combined with work in specific knowledge domains, from nursing to economics, graduates of Michigan's Informatics major are vital in leading organizations to harness emerging technologies. The deep understanding of the connections between information technology, data analysis, and organizations and society is also excellent background for students seeking to enter law school, business school, medical school, or schools of public policy. And, depending on the track they complete, students are well prepared for graduate study in many fields, including statistics, computer science, information, law, medicine, public health, and natural and social sciences.

 

Summary of Course Requirements and Prerequisites

The major in Informatics requires 40 credit hours for completion, including four core courses, 3-4 courses in one of four flexible tracks, plus electives selected from a list of recommended courses.

Four prerequisite courses serve as an introduction to core academic aspects of the curriculum and are required of all concentators. The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Core courses may be taken in any order and are required for completion of the major. Students may enroll in track courses before they have completed the entire core curriculum.

In pursuing the major in Informatics, students have the flexibility to specialize in one of four tracks: Computational Informatics, Information Analysis, Internet Informatics, Life Science Informatics, or Social Computing. Each of the five tracks requires three to four courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the track.

In addition to the major's core and track requirements, students select major electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Committee for the major will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

  1. Data Mining & Information Analysis Track
    The collection, analysis, and visualization of complex data play critical roles in research, business, and government. Powerful tools from applied statistics, mathematics, and computational science can be used to uncover the meaning behind complex data sets. The Data Mining and Information Analysis track integrates these disciplines to provide students with practical skills and a theoretical basis for approaching challenging data analysis problems. Students in this track learn how to develop and test models for making predictions, to search through large collections of data for rare and unexpected patterns, and to characterize the degree of certainty associated with discoveries made in the course of data analysis. Skills and knowledge acquired in this track are increasingly important in the job market and are highly relevant for a number of graduate school programs.
  2. Computational Informatics Track (At the end of Fall 2013 declaration to this track will be discontinued)
    Today, computer technology is ubiquitous, and a robust understanding of information systems is important in almost every industry and organization. Computational Informatics emphasizes issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure. In the Computational Informatics track, students learn to assess and build usable software applications for web servers, browsers, smartphones, information analysis tools, and automation of common activities. They develop analytical skills and gain a professional understanding of how people and organizations utilize technology to manage data. Graduates of this track put their skills to use in business and in the financial, software development, and information technology industries. They are also well prepared for graduate programs in computing and information sciences, among others.Note: This track is scheduled to be phased out in the near future and be replaced by the Internet Informatics Track.
  3.  Life Science Informatics Track
    Using artificial information systems, scientists have made great progress in identifying core components of organisms and ecosystems and are beginning to better understand how these components behave and interact with each other. In fact, biology has become an information science, as computational techniques have become an important means to develop and evaluate biological hypotheses. Informatics is used from basic biological research-studying how patterns of gene expression differ across various cell types-to the practice of medicine, where informatics is used to compare treatments, to identify social correlates of health, and to evaluate possible changes in health policy. The Life Science Informatics track prepares students for careers and advanced study in a number of information-related fields in the life sciences, as well as medical school and other areas of graduate study.
  4. Social Computing Track (this track will be phased out in Fall 2013; students considering Social Computing beyond this date should apply for the B.S. in Information)
    Facebook, Twitter, and shared calendars are now embedded in the fabric of everyday life, but countless other applications have yet to be discovered and perfected, each potentially enhancing the way we interact. Applying knowledge from psychology, economics, and sociology, Social Computing students craft, evaluate, and refine social software computer applications for engaging technology in unique social contexts. Advances in computing have created opportunities for studying patterns of social interaction and developing systems that act as introducers, recommenders, coordinators, and record-keepers. Students in this track develop analytical and problem-solving skills useful in business, software development, and the information industry and are prepared for graduate study in areas including information science, business, and law.

Field of Major

For purposes of calculating grade point average, the term "field of the major" means the following:

  1. All STATS courses.
  2. All courses used to meet requirements for the major.
  3. All mandatory major prerequisites.

Informatics majors may not use any STATS courses toward the Area Distribution requirement.

Note. It is not necessary to complete all prerequisite courses prior to declaring an Informatics major.  Minimum grade for all prerequisite courses is a C.

Prerequisites to Core Courses

  1. SI 110 / SOC 110 with a C or better;
  2. MATH 115 with a C or better;
  3. EECS 182 / SI 182 or EECS 183 with a C or better;
  4. STATS 250 with a C or better;

Prerequisite to Declaration

MATH 115, STATS 250, and EECS 182 or 183.

Program of study in a major

A minimum of 12 courses and a minimum of 40 credits.

  1. Core: EECS 203, EECS 280, STATS 403
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track :
      1. EECS 382
      2. Two of the following Computational/Quantitative courses: EECS 281 and one of 376, 388, 476, 477, 481, 484, 485, 492, 493, 494.
      3. Electives*: 8 credits must be elected at the 300-level or higher
    2. Data Mining & Information Analysis track :
      1. MATH 217
      2. STATS 406
      3. STATS 415
      4. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      5. Electives*: 8 credits must be elected at the 300-level or higher
    3. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses:
        • BIOLOGY 305
        • MCDB 310
      3. Two of the following Quantitative/Computational courses:
        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 12-14 credits; 4 credits must be elected at the 300-level or higher
    4. Social Computing track :
      1. PSYCH 280
      2. SI 301
      3. SI 422
      4. SI 429 (or 529)
      5. Electives* 8 credits must be elected at the 300-level or higher
  3. Electives: Additional Informatics electives to bring total major credits to 40 credits (44 for Data Mining track). The number of electives required for each track varies, depending on the number of required core courses in the track. Informatics majors be allowed to select their electives from one of the following lists of courses, depending on their chosen track. Students who wish to use an elective that is not on this list should consult their track advisor before taking the course.  

 

Informatics Pre-Approved Electives

Students may chose electives for their declared track from the following pre-approved lists of electives without consultation of the track advisor.

Note: Only one elective course in a track indicated with "*" can be taken for elective credit.   

Course Internet Informatics / Computational Informatics Data Mining & Information Analysis Life Science Informatics Social Computing
BIOINF 463/MATH 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOINF 527 Introduction to Bioinformatics & Computational Biology    Data Mining & Information Analysis *    
BIOINF 545/STATS 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
BIOINF 547/MATH 547/STATS 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
BIOINF 551/BIOLCHEM 551/CHEM 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOMEDE 551/BIOLCHEM 551/CHEM 551/BIOINF 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOPHYS 463/MATH 463/BIOINF 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 449/STATS 449 Topics in Biostatistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 503 Introduction to Biostatistics        Social Computing *
BIOSTAT 646/BIOINF 545/STATS 545/Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
CHEM 551/BIOLCHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
CMPLXSYS 510/MATH 550 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
EEB 485 Population and Community Ecology      Life Science Informatics *  
EECS 281 Data Structures and Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 376 Foundations of Computer Science  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 382 Internet‐scale Computing    Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 388 Security course  Computational Informatics      
EECS 476 Theory of Internet Applications  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 477 Introduction to Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 481 Software Engineering  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 484 Database Management Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 485 Web Database and Information Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 487 Interactive Computer Graphics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 489 Computer Networks  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 492 Introduction to Artificial Intelligence  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 493 User Interface Development  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 494 Computer Game Design and Development  Internet Informatics / Computational Informatics     Social Computing 
EECS 495 Patent Fundamentals for Engineers        Social Computing 
HONORS 352. Honors Introduction to Research in the Natural Sciences, section titled "Cyberscience"   Data Mining & Information Analysis Life Science Informatics Social Computing 
         
IOE 310 Introduction to Optimization Methods        Social Computing *
IOE 510/MATH 561/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
IOE 511/MATH 562 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
IOE 512 Dynamic Programming    Data Mining & Information Analysis *   Social Computing *
MATH 416 Theory of Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 425/STATS 425 Introduction to Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 433 Introduction to Differential Geometry    Data Mining & Information Analysis     
MATH 451 Advanced Calculus I    Data Mining & Information Analysis  Life Science Informatics   
MATH 462 Mathematical Models    Data Mining & Information Analysis  Life Science Informatics   
MATH 463/BIOINF 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
MATH 471 Introduction to Numerical Methods    Data Mining & Information Analysis  Life Science Informatics   
MATH 525/STATS 525 Probability Theory  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 526 Discrete State Stochastic Processes  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 547/BIOINF 547/STATS 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
MATH 548/STATS 548 Computations in Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
MATH 550/CMPLXSYS 510 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
MATH 561/IOE 510/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
MATH 562/IOE 511 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
MATH 571 Numerical Methods for Scientific Computing I    Data Mining & Information Analysis     
MCDB 408 Genomic Biology    Data Mining & Information Analysis  Life Science Informatics   
MCDB 411 Protein Structure and Function      Life Science Informatics   
OMS 518/IOE 510/MATH 561 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
PATH 551/BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
SI 301 Models of Social Information Processing  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics *  
SI 422 Evaluation of Systems and Services  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics *  
SI 429 eCommunities: Analysis & Design of Online Interaction Environments  Internet Informatics / Computational Informatics      
SI 508 Networks: Theory and Application  Internet Informatics / Computational Informatics Data Mining & Information Analysis    Social Computing 
SI 532 Digital Government I: Information Technology and Democratic Politics  Internet Informatics / Computational Informatics *     Social Computing *
SI 539 Design of Complex Websites  Internet Informatics / Computational Informatics     Social Computing 
SI 572 Database Design  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics  Social Computing 
SI 583 Recommender Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis *   Social Computing 
SI 631 Practical l Engagement Workshop: Content Management Systems    Data Mining & Information Analysis * Life Science Informatics * Social Computing 
SI 679 Aggregation and Prediction Markets    Data Mining & Information Analysis *   Social Computing 
SI 683 Reputation Systems    Data Mining & Information Analysis *   Social Computing 
SI 689 Computer Supported Cooperative Work  Internet Informatics / Computational Informatics * Data Mining & Information Analysis * Life Science Informatics * Social Computing *
STATS 401 Applied Statistical Methods II  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 406 Introduction to Statistical Computing  Internet Informatics / Computational Informatics   Life Science Informatics  Social Computing 
STATS 408 Statistical Principles for Problem Solving: A Systems Approach  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 415 Data Mining  Internet Informatics / Computational Informatics   Life Science Informatics  Social Computing 
STATS 425/MATH 425 Introduction to Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 426 Introduction to Theoretical Statistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 430 Applied Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 449/BIOSTAT 449 Topics in Biostatistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
STATS 470 Introduction to the Design of Experiments  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 480 Survey Sampling Techniques  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 500 Applied Statistics I  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 525/MATH 525 Probability Theory  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 526/MATH 526 Discrete State Stochastic Processes  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 545/BIOINF 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
STATS 547/MATH 547/BIOINF 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
STATS 548/MATH 548 Computations in Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   

Honors Plan

Students interested in doing original research in informatics are encouraged to consider the Informatics Honors Plan. The Honors major is open to all Informatics majors who have achieved both a major GPA and an overall GPA of 3.4 or better. At least one year prior to graduation, interested students should identify a member of the U-M faculty with informatics expertise to serve as their faculty advisor.  Together with that person, the student prepares a 2-3 paragraph summary of the proposed thesis project, which is submitted together with the Honors Plan Application to the Informatics Program Coordinator for review by department advisors.  The student completes the thesis work in the senior year, while enrolling in 3-4 credits of independent study (such as EECS 499, MATH 399, SI 491, STATS 489, HONORS 390, or HONORS 490).  At least six weeks before the last day of classes in the term in which the student will complete the independent study and thesis, an electronic copy of the final Honors thesis is submitted to the Informatics program coordinator. The faculty advisor then solicits comments on the completed thesis from an independent reader, and the student presents the work in a public forum.

 

Informatics Major (Winter 2013-Summer 2013) +

May be elected as an interdepartmental concentration program.

Effective Winter 2013-Summer 2013

What is Informatics?

Informatics is the study of human and computer information processing systems from a socio-technical perspective. Michigan's unique interdisciplinary approach to this growing field of research and teaching emphasizes a solid grounding in contemporary computer programming, mathematics, and statistics, combined with study of the ethical and social science aspects of complex information systems. Experts in the field help design new information technology tools informed by scientific, business, and cultural contexts.

Informatics is where the technical accomplishments of computer science, mathematics, and statistics become embedded in the ways we interact, imagine, and produce in richer and more thoughtful ways. Students will obtain software development skills and learn a formal framework for making inferences from experimental and observational data, focusing on the manner and purpose in which people interact with information and information systems.

The concentration in Informatics is appropriate for students with varied interests and a range of background knowledge in information systems engineering, information analysis, and/or the use of information processing in biological, societal and emerging application areas. Students who complete the concentration are equipped to participate fully in important emerging areas such as bioinformatics, information analysis, large-scale information management, and human-centered information systems design. In addition, depending on which track a student selects, he or she develops the intellectual skills

  • to analyze enormous quantities of information (Information Analysis Track); 
  • to reason systematically about the social impacts of and on information systems (Social Computing Track); 
  • to reason about the design of information systems (Computational Informatics Track); or 
  • to apply information technology to large-scale, cutting-edge problems in the life sciences (Life Science Informatics Track).

Students concentrating in Informatics have many opportunities available to them after graduation. The concentration provides excellent preparation for jobs in the IT industry as product managers, human factors engineers, usability specialists, information analysts in sciences and science related industries, and designers working with large software development teams. Recruiters visiting the university frequently are seeking students with the ideals and skill sets that are provided by this program. Combined with work in specific knowledge domains, from nursing to economics, graduates of Michigan's Informatics concentration are vital in leading organizations to harness emerging technologies. The deep understanding of the connections between information technology, data analysis, and organizations and society is also excellent background for students seeking to enter law school, business school, medical school, or schools of public policy. And, depending on the track they complete, students are well prepared for graduate study in many fields, including statistics, computer science, information, law, medicine, public health, and natural and social sciences.

 

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 40 credit hours for completion, including four core courses, 3-4 courses in one of four flexible tracks, plus electives selected from a list of recommended courses.

Four prerequisite courses serve as an introduction to core academic aspects of the curriculum and are required of all concentators. The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Core courses may be taken in any order and are required for completion of the concentration. Students may enroll in track courses before they have completed the entire core curriculum.

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Computational Informatics, Information Analysis, Internet Informatics, Life Science Informatics, or Social Computing. Each of the five tracks requires three to four courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the track.

In addition to the concentration's core and track requirements, students select concentration electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Committee for the concentration will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

  1. Data Mining & Information Analysis Track
    The collection, analysis, and visualization of complex data play critical roles in research, business, and government. Powerful tools from applied statistics, mathematics, and computational science can be used to uncover the meaning behind complex data sets. The Data Mining and Information Analysis track integrates these disciplines to provide students with practical skills and a theoretical basis for approaching challenging data analysis problems. Students in this track learn how to develop and test models for making predictions, to search through large collections of data for rare and unexpected patterns, and to characterize the degree of certainty associated with discoveries made in the course of data analysis. Skills and knowledge acquired in this track are increasingly important in the job market and are highly relevant for a number of graduate school programs.
  2. Computational Informatics Track (At the end of Fall 2013 declaration to this track will be discontinued)
    Today, computer technology is ubiquitous, and a robust understanding of information systems is important in almost every industry and organization. Computational Informatics emphasizes issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure. In the Computational Informatics track, students learn to assess and build usable software applications for web servers, browsers, smartphones, information analysis tools, and automation of common activities. They develop analytical skills and gain a professional understanding of how people and organizations utilize technology to manage data. Graduates of this track put their skills to use in business and in the financial, software development, and information technology industries. They are also well prepared for graduate programs in computing and information sciences, among others.Note: This track is scheduled to be phased out in the near future and be replaced by the Internet Informatics Track.
  3. Internet Informatics (At the end of Fall 2012 declaration to this track will be discontinued)
    Internet is the foundation of today's information systems. Social networks, cloud services, and mobile applications are all enabled by the Internet. This is an applied track in which students experiment with technologies behind Internet-based information systems and acquire skills to map problems to deployable Internet-based solutions. The students in the Internet Informatics track are prepared for careers in industries that make use of information technology as software consultants, IT specialists, app developers , and system architects. Students can also go on for advanced studies in information-related fields, computer science, business, and law.
  4.  Life Science Informatics Track
    Using artificial information systems, scientists have made great progress in identifying core components of organisms and ecosystems and are beginning to better understand how these components behave and interact with each other. In fact, biology has become an information science, as computational techniques have become an important means to develop and evaluate biological hypotheses. Informatics is used from basic biological research-studying how patterns of gene expression differ across various cell types-to the practice of medicine, where informatics is used to compare treatments, to identify social correlates of health, and to evaluate possible changes in health policy. The Life Science Informatics track prepares students for careers and advanced study in a number of information-related fields in the life sciences, as well as medical school and other areas of graduate study.
  5. Social Computing Track (this track will be phased out in Fall 2013; students considering Social Computing beyond this date should apply for the B.S. in Information)
    Facebook, Twitter, and shared calendars are now embedded in the fabric of everyday life, but countless other applications have yet to be discovered and perfected, each potentially enhancing the way we interact. Applying knowledge from psychology, economics, and sociology, Social Computing students craft, evaluate, and refine social software computer applications for engaging technology in unique social contexts. Advances in computing have created opportunities for studying patterns of social interaction and developing systems that act as introducers, recommenders, coordinators, and record-keepers. Students in this track develop analytical and problem-solving skills useful in business, software development, and the information industry and are prepared for graduate study in areas including information science, business, and law.

Field of Concentration

For purposes of calculating grade point average, the term "field of concentration" means the following:

  1. All STATS courses.
  2. All courses used to meet concentration requirements.
  3. All mandatory concentration prerequisites.

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

Note. It is not necessary to complete all prerequisite courses prior to declaring an Informatics concentration.  Minimum grade for all prerequisite courses is a C.

Prerequisites to Core Courses

  1. SI 110 / SOC 110 with a C or better;
  2. MATH 115 with a C or better;
  3. EECS 182 / SI 182 or EECS 183 with a C or better;
  4. STATS 250 with a C or better;

Prerequisite to Declaration

MATH 115, STATS 250, and EECS 182 or 183.

Concentration Program

A minimum of 12 courses and a minimum of 40 credits.

  1. Core: EECS 203, EECS 282 or 280*, STATS 403
    *If a student takes both EECS 282 and 280, EECS 280 will be treated as an elective.
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track :
      1. EECS 280 (note: students who did not take EECS 282 will need to take an additional 4 credits of electives)
      2. EECS 382
      3. Two of the following Computational/Quantitative courses: EECS 281 and one of 376, 388, 476, 477, 481, 484, 485, 492, 493, 494.
      4. Electives*: 8 credits must be elected at the 300-level or higher
    2. Data Mining & Information Analysis track :
      1. MATH 217
      2. STATS 406
      3. STATS 415
      4. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      5. Electives*: 8 credits must be elected at the 300-level or higher
    3. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses:
        • BIOLOGY 305
        • MCDB 310
      3. Two of the following Quantitative/Computational courses:
        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 12-14 credits; 4 credits must be elected at the 300-level or higher
    4. Social Computing track :
      1. PSYCH 280
      2. SI 301
      3. SI 422
      4. SI 429 (or 529)
      5. Electives* 8 credits must be elected at the 300-level or higher
  3. Electives: Additional Informatics electives to bring total concentration credits to 40 credits (44 for Data Mining track). The number of electives required for each track varies, depending on the number of required core courses in the track. Informatics concentrators be allowed to select their electives from one of the following lists of courses, depending on their chosen track. Students who wish to use an elective that is not on this list should consult their track advisor before taking the course.  

 

Informatics Pre-Approved Electives

Students may chose electives for their declared track from the following pre-approved lists of electives without consultation of the track advisor.

Note: Only one elective course in a track indicated with "*" can be taken for elective credit.   

Course Internet Informatics / Computational Informatics Data Mining & Information Analysis Life Science Informatics Social Computing
BIOINF 463/MATH 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOINF 527 Introduction to Bioinformatics & Computational Biology    Data Mining & Information Analysis *    
BIOINF 545/STATS 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
BIOINF 547/MATH 547/STATS 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
BIOINF 551/BIOLCHEM 551/CHEM 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOMEDE 551/BIOLCHEM 551/CHEM 551/BIOINF 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOPHYS 463/MATH 463/BIOINF 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 449/STATS 449 Topics in Biostatistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 503 Introduction to Biostatistics        Social Computing *
BIOSTAT 646/BIOINF 545/STATS 545/Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
CHEM 551/BIOLCHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
CMPLXSYS 510/MATH 550 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
EEB 485 Population and Community Ecology      Life Science Informatics *  
EECS 280 Programming and Introductory Data Structures  Internet Informatics Data Mining & Information Analysis Life Science Informatics Social Computing 
EECS 281 Data Structures and Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 376 Foundations of Computer Science  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 382 Internet‐scale Computing    Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 388 Security course  Computational Informatics      
EECS 476 Theory of Internet Applications  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 477 Introduction to Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 481 Software Engineering  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 484 Database Management Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 485 Web Database and Information Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 487 Interactive Computer Graphics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 489 Computer Networks  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 492 Introduction to Artificial Intelligence  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 493 User Interface Development  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 494 Computer Game Design and Development  Internet Informatics / Computational Informatics     Social Computing 
EECS 495 Patent Fundamentals for Engineers        Social Computing 
HONORS 352. Honors Introduction to Research in the Natural Sciences, section titled "Cyberscience"   Data Mining & Information Analysis Life Science Informatics Social Computing 
         
IOE 310 Introduction to Optimization Methods        Social Computing *
IOE 510/MATH 561/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
IOE 511/MATH 562 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
IOE 512 Dynamic Programming    Data Mining & Information Analysis *   Social Computing *
MATH 416 Theory of Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 425/STATS 425 Introduction to Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 433 Introduction to Differential Geometry    Data Mining & Information Analysis     
MATH 451 Advanced Calculus I    Data Mining & Information Analysis  Life Science Informatics   
MATH 462 Mathematical Models    Data Mining & Information Analysis  Life Science Informatics   
MATH 463/BIOINF 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
MATH 471 Introduction to Numerical Methods    Data Mining & Information Analysis  Life Science Informatics   
MATH 525/STATS 525 Probability Theory  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 526 Discrete State Stochastic Processes  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 547/BIOINF 547/STATS 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
MATH 548/STATS 548 Computations in Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
MATH 550/CMPLXSYS 510 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
MATH 561/IOE 510/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
MATH 562/IOE 511 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
MATH 571 Numerical Methods for Scientific Computing I    Data Mining & Information Analysis     
MCDB 408 Genomic Biology    Data Mining & Information Analysis  Life Science Informatics   
MCDB 411 Protein Structure and Function      Life Science Informatics   
OMS 518/IOE 510/MATH 561 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
PATH 551/BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
SI 301 Models of Social Information Processing  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics *  
SI 422 Evaluation of Systems and Services  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics *  
SI 429 eCommunities: Analysis & Design of Online Interaction Environments  Internet Informatics / Computational Informatics      
SI 508 Networks: Theory and Application  Internet Informatics / Computational Informatics Data Mining & Information Analysis    Social Computing 
SI 532 Digital Government I: Information Technology and Democratic Politics  Internet Informatics / Computational Informatics *     Social Computing *
SI 539 Design of Complex Websites  Internet Informatics / Computational Informatics     Social Computing 
SI 572 Database Design  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics  Social Computing 
SI 583 Recommender Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis *   Social Computing 
SI 631 Practical l Engagement Workshop: Content Management Systems    Data Mining & Information Analysis * Life Science Informatics * Social Computing 
SI 679 Aggregation and Prediction Markets    Data Mining & Information Analysis *   Social Computing 
SI 683 Reputation Systems    Data Mining & Information Analysis *   Social Computing 
SI 689 Computer Supported Cooperative Work  Internet Informatics / Computational Informatics * Data Mining & Information Analysis * Life Science Informatics * Social Computing *
STATS 401 Applied Statistical Methods II  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 406 Introduction to Statistical Computing  Internet Informatics / Computational Informatics   Life Science Informatics  Social Computing 
STATS 408 Statistical Principles for Problem Solving: A Systems Approach  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 415 Data Mining  Internet Informatics / Computational Informatics   Life Science Informatics  Social Computing 
STATS 425/MATH 425 Introduction to Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 426 Introduction to Theoretical Statistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 430 Applied Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 449/BIOSTAT 449 Topics in Biostatistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
STATS 470 Introduction to the Design of Experiments  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 480 Survey Sampling Techniques  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 500 Applied Statistics I  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 525/MATH 525 Probability Theory  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 526/MATH 526 Discrete State Stochastic Processes  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 545/BIOINF 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
STATS 547/MATH 547/BIOINF 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
STATS 548/MATH 548 Computations in Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   

Honors Concentration

Students interested in doing original research in informatics are encouraged to consider the Informatics Honors Concentration. The Honors concentration is open to all Informatics concentrators who have achieved both a concentration GPA and an overall GPA of 3.4 or better. At least one year prior to graduation, interested students should identify a member of the U-M faculty with informatics expertise to serve as their faculty advisor.  Together with that person, the student prepares a 2-3 paragraph summary of the proposed thesis project, which is submitted together with the Honors Concentration Application to the Informatics Program Coordinator for review by concentration advisors.  The student completes the thesis work in the senior year, while enrolling in 3-4 credits of independent study (such as EECS 499, MATH 399, SI 491, STATS 489, HONORS 390, or HONORS 490).  At least six weeks before the last day of classes in the term in which the student will complete the independent study and thesis, an electronic copy of the final Honors thesis is submitted to the Informatics program coordinator. The faculty advisor then solicits comments on the completed thesis from an independent reader, and the student presents the work in a public forum.

 

Informatics Major (Fall 2012) +

May be elected as an interdepartmental concentration program.

Effective Fall 2012

What is Informatics?

Informatics is the study of human and computer information processing systems from a socio-technical perspective. Michigan's unique interdisciplinary approach to this growing field of research and teaching emphasizes a solid grounding in contemporary computer programming, mathematics, and statistics, combined with study of the ethical and social science aspects of complex information systems. Experts in the field help design new information technology tools informed by scientific, business, and cultural contexts.

Informatics is where the technical accomplishments of computer science, mathematics, and statistics become embedded in the ways we interact, imagine, and produce in richer and more thoughtful ways. Students will obtain software development skills and learn a formal framework for making inferences from experimental and observational data, focusing on the manner and purpose in which people interact with information and information systems.

The concentration in Informatics is appropriate for students with varied interests and a range of background knowledge in information systems engineering, information analysis, and/or the use of information processing in biological, societal and emerging application areas. Students who complete the concentration are equipped to participate fully in important emerging areas such as bioinformatics, information analysis, large-scale information management, and human-centered information systems design. In addition, depending on which track a student selects, he or she develops the intellectual skills

  • to analyze enormous quantities of information (Information Analysis Track); 
  • to reason systematically about the social impacts of and on information systems (Social Computing Track); 
  • to apply information technology to the design of Internet-based solutions (Internet Informatics);
  • to reason about the design of information systems (Computational Informatics Track); or 
  • to apply information technology to large-scale, cutting-edge problems in the life sciences (Life Science Informatics Track).

Students concentrating in Informatics have many opportunities available to them after graduation. The concentration provides excellent preparation for jobs in the IT industry as product managers, human factors engineers, usability specialists, information analysts in sciences and science related industries, and designers working with large software development teams. Recruiters visiting the university frequently are seeking students with the ideals and skill sets that are provided by this program. Combined with work in specific knowledge domains, from nursing to economics, graduates of Michigan's Informatics concentration are vital in leading organizations to harness emerging technologies. The deep understanding of the connections between information technology, data analysis, and organizations and society is also excellent background for students seeking to enter law school, business school, medical school, or schools of public policy. And, depending on the track they complete, students are well prepared for graduate study in many fields, including statistics, computer science, information, law, medicine, public health, and natural and social sciences.

Informatics Student Organization (ISO). The Informatics Student Organization is dedicated to the advancement and development of society by engaging in projects that consider new approaches to dealing with contemporary, societal problems. Through the developing field of information science, we will attempt to apply our collective knowledge to innovation.

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 44 credit hours for completion, including four core courses, 3-4 courses in one of four flexible tracks, plus electives selected from a list of recommended courses.

Four prerequisite courses serve as an introduction to core academic aspects of the curriculum and are required of all concentators. The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Core courses may be taken in any order and are required for completion of the concentration. Students may enroll in track courses before they have completed the entire core curriculum.

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Computational Informatics, Information Analysis, Internet Informatics, Life Science Informatics, or Social Computing. Each of the five tracks requires three to four courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the track.

In addition to the concentration's core and track requirements, students select concentration electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Committee for the concentration will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

  1. Computational Informatics Track
    (At the end of Fall 2012 this track will be discontinued)
    Today, computer technology is ubiquitous, and a robust understanding of information systems is important in almost every industry and organization. Computational Informatics emphasizes issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure. In the Computational Informatics track, students learn to assess and build usable software applications for web servers, browsers, smartphones, information analysis tools, and automation of common activities. They develop analytical skills and gain a professional understanding of how people and organizations utilize technology to manage data. Graduates of this track put their skills to use in business and in the financial, software development, and information technology industries. They are also well prepared for graduate programs in computing and information sciences, among others.Note: This track is scheduled to be phased out in the near future and be replaced by the Internet Informatics Track.
  2. Data Mining & Information Analysis Track
    The collection, analysis, and visualization of complex data play critical roles in research, business, and government. Powerful tools from applied statistics, mathematics, and computational science can be used to uncover the meaning behind complex data sets. The Data Mining and Information Analysis track integrates these disciplines to provide students with practical skills and a theoretical basis for approaching challenging data analysis problems. Students in this track learn how to develop and test models for making predictions, to search through large collections of data for rare and unexpected patterns, and to characterize the degree of certainty associated with discoveries made in the course of data analysis. Skills and knowledge acquired in this track are increasingly important in the job market and are highly relevant for a number of graduate school programs.
  3. Internet Informatics
    Internet is the foundation of today's information systems. Social networks, cloud services, and mobile applications are all enabled by the Internet. This is an applied track in which students experiment with technologies behind Internet-based information systems and acquire skills to map problems to deployable Internet-based solutions. The students in the Internet Informatics track are prepared for careers in industries that make use of information technology as software consultants, IT specialists, app developers , and system architects. Students can also go on for advanced studies in information-related fields, computer science, business, and law.
  4.  Life Science Informatics Track
    Using artificial information systems, scientists have made great progress in identifying core components of organisms and ecosystems and are beginning to better understand how these components behave and interact with each other. In fact, biology has become an information science, as computational techniques have become an important means to develop and evaluate biological hypotheses. Informatics is used from basic biological research-studying how patterns of gene expression differ across various cell types-to the practice of medicine, where informatics is used to compare treatments, to identify social correlates of health, and to evaluate possible changes in health policy. The Life Science Informatics track prepares students for careers and advanced study in a number of information-related fields in the life sciences, as well as medical school and other areas of graduate study.
  5. Social Computing Track
    Facebook, Twitter, and shared calendars are now embedded in the fabric of everyday life, but countless other applications have yet to be discovered and perfected, each potentially enhancing the way we interact. Applying knowledge from psychology, economics, and sociology, Social Computing students craft, evaluate, and refine social software computer applications for engaging technology in unique social contexts. Advances in computing have created opportunities for studying patterns of social interaction and developing systems that act as introducers, recommenders, coordinators, and record-keepers. Students in this track develop analytical and problem-solving skills useful in business, software development, and the information industry and are prepared for graduate study in areas including information science, business, and law.

Field of Concentration

For purposes of calculating grade point average, the term "field of concentration" means the following:

  1. All STATS courses.
  2. All courses used to meet concentration requirements.
  3. All mandatory concentration prerequisites.

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

Prerequisites to Core Courses

  1. SI 110 / SOC 110;
  2. MATH 115;
  3. EECS 182 / SI 182;
  4. STATS 250 (or 350) or STATS 400.

Concentration Program

A minimum of 12 courses and 44 credits.

  1. Core: EECS 203, EECS 282, STATS 403 , and SI 410.
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track :
      1. EECS 382
      2. EECS 280
      3. Two of the following Computational/Quantitative courses:
        EECS 281, 376, 476, 477, 481, 484, 485, 492, 493, 494.
      4. Electives*: 8 credits must be elected at the 300-level or higher
    2. Data Mining & Information Analysis track :
      1. STATS 406
      2. STATS 415
      3. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      4. Electives*: 8 credits must be elected at the 300-level or higher
    3. Internet Informatics track
      1. EECS 382
      2. EECS 485
      3. EECS 398, section titled "Information Security"
      4. Four wide technical electives (16 credits)
    4. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses:
        • BIOLOGY 305
        • MCDB 310
      3. Two of the following Quantitative/Computational courses:
        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 12-14 credits; 4 credits must be elected at the 300-level or higher
    5. Social Computing track :
      1. PSYCH 280
      2. SI 301
      3. SI 422
      4. SI 429 (or 529)
      5. Electives* 8 credits must be elected at the 300-level or higher
  3. Electives: Additional Informatics electives to bring total concentration credits to 44 credits.The number of electives required for each track varies, depending on the number of required core courses in the track.Informatics concentrators be allowed to select their electives from one of the following lists of courses, depending on their chosen track. Students who wish to use an elective that is not on this list should consult their track advisor before taking the course.  

 

Informatics Pre-Approved Electives

Students may chose electives for their declared track from the following pre-approved lists of electives without consultation of the track advisor.

Note: Only one elective course in a track indicated with "*" can be taken for elective credit.   

Course Internet Informatics / Computational Informatics Data Mining & Information Analysis Life Science Informatics Social Computing
BIOINF 463/MATH 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOINF 527 Introduction to Bioinformatics & Computational Biology    Data Mining & Information Analysis *    
BIOINF 545/STATS 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
BIOINF 547/MATH 547/STATS 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
BIOINF 551/BIOLCHEM 551/CHEM 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOMEDE 551/BIOLCHEM 551/CHEM 551/BIOINF 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOPHYS 463/MATH 463/BIOINF 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 449/STATS 449 Topics in Biostatistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 503 Introduction to Biostatistics        Social Computing *
BIOSTAT 646/BIOINF 545/STATS 545/Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
CHEM 551/BIOLCHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
CMPLXSYS 510/MATH 550 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
EEB 485 Population and Community Ecology      Life Science Informatics *  
EECS 280 Programming and Introductory Data Structures  Internet Informatics     Social Computing 
EECS 281 Data Structures and Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 376 Foundations of Computer Science  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 382 Internet‐scale Computing    Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 476 Theory of Internet Applications  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 477 Introduction to Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 481 Software Engineering  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 484 Database Management Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 485 Web Database and Information Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 487 Interactive Computer Graphics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 489 Computer Networks  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 492 Introduction to Artificial Intelligence  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 493 User Interface Development  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 494 Computer Game Design and Development  Internet Informatics / Computational Informatics     Social Computing 
EECS 495 Patent Fundamentals for Engineers        Social Computing 
HONORS 352. Honors Introduction to Research in the Natural Sciences, section titled "Cyberscience"   Data Mining & Information Analysis Life Science Informatics Social Computing 
         
IOE 310 Introduction to Optimization Methods        Social Computing *
IOE 510/MATH 561/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
IOE 511/MATH 562 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
IOE 512 Dynamic Programming    Data Mining & Information Analysis *   Social Computing *
MATH 416 Theory of Algorithms  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 425/STATS 425 Introduction to Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 433 Introduction to Differential Geometry    Data Mining & Information Analysis     
MATH 451 Advanced Calculus I    Data Mining & Information Analysis  Life Science Informatics   
MATH 462 Mathematical Models    Data Mining & Information Analysis  Life Science Informatics   
MATH 463/BIOINF 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
MATH 471 Introduction to Numerical Methods    Data Mining & Information Analysis  Life Science Informatics   
MATH 525/STATS 525 Probability Theory  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 526 Discrete State Stochastic Processes  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 547/BIOINF 547/STATS 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
MATH 548/STATS 548 Computations in Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
MATH 550/CMPLXSYS 510 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
MATH 561/IOE 510/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
MATH 562/IOE 511 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
MATH 571 Numerical Methods for Scientific Computing I    Data Mining & Information Analysis     
MCDB 408 Genomic Biology    Data Mining & Information Analysis  Life Science Informatics   
MCDB 411 Protein Structure and Function      Life Science Informatics   
OMS 518/IOE 510/MATH 561 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
PATH 551/BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
SI 301 Models of Social Information Processing  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics *  
SI 422 Evaluation of Systems and Services  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics *  
SI 429 eCommunities: Analysis & Design of Online Interaction Environments  Internet Informatics / Computational Informatics      
SI 508 Networks: Theory and Application  Internet Informatics / Computational Informatics Data Mining & Information Analysis    Social Computing 
SI 532 Digital Government I: Information Technology and Democratic Politics  Internet Informatics / Computational Informatics *     Social Computing *
SI 539 Design of Complex Websites  Internet Informatics / Computational Informatics     Social Computing 
SI 572 Database Design  Internet Informatics / Computational Informatics Data Mining & Information Analysis * Life Science Informatics  Social Computing 
SI 583 Recommender Systems  Internet Informatics / Computational Informatics Data Mining & Information Analysis *   Social Computing 
SI 631 Practical l Engagement Workshop: Content Management Systems    Data Mining & Information Analysis * Life Science Informatics * Social Computing 
SI 679 Aggregation and Prediction Markets    Data Mining & Information Analysis *   Social Computing 
SI 683 Reputation Systems    Data Mining & Information Analysis *   Social Computing 
SI 689 Computer Supported Cooperative Work  Internet Informatics / Computational Informatics * Data Mining & Information Analysis * Life Science Informatics * Social Computing *
STATS 401 Applied Statistical Methods II  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 406 Introduction to Statistical Computing  Internet Informatics / Computational Informatics   Life Science Informatics  Social Computing 
STATS 408 Statistical Principles for Problem Solving: A Systems Approach  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 415 Data Mining  Internet Informatics / Computational Informatics   Life Science Informatics  Social Computing 
STATS 425/MATH 425 Introduction to Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 426 Introduction to Theoretical Statistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 430 Applied Probability  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 449/BIOSTAT 449 Topics in Biostatistics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
STATS 470 Introduction to the Design of Experiments  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 480 Survey Sampling Techniques  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 500 Applied Statistics I  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 525/MATH 525 Probability Theory  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 526/MATH 526 Discrete State Stochastic Processes  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 545/BIOINF 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
STATS 547/MATH 547/BIOINF 547 Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics Data Mining & Information Analysis  Life Science Informatics   
STATS 548/MATH 548 Computations in Probabilistic Modeling in Bioinformatics  Internet Informatics / Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   

Honors Concentration

Students interested in doing original research in informatics are encouraged to consider the Informatics Honors Concentration. The Honors concentration is open to all Informatics concentrators who have achieved both a concentration GPA and an overall GPA of 3.4 or better. At least one year prior to graduation, interested students should identify a member of the U-M faculty with informatics expertise to serve as their faculty advisor.  Together with that person, the student prepares a 2-3 paragraph summary of the proposed thesis project, which is submitted together with the Honors Concentration Application to the Informatics Program Coordinator for review by concentration advisors.  The student completes the thesis work in the senior year, while enrolling in 3-4 credits of independent study (such as EECS 499, MATH 399, SI 491, STATS 489, HONORS 390, or HONORS 490).  At least six weeks before the last day of classes in the term in which the student will complete the independent study and thesis, an electronic copy of the final Honors thesis is submitted to the Informatics program coordinator. The faculty advisor then solicits comments on the completed thesis from an independent reader, and the student presents the work in a public forum.

 

Informatics concentration (Winter 2012-Summer 2012) +

May be elected as an interdepartmental concentration program

Effective Winter 2012-Summer 2012 

 

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 44 credit hours for completion, including four core courses, 3-4 courses in one of four flexible tracks, plus electives selected from a list of recommended courses.

Four prerequisite courses serve as an introduction to core academic aspects of the curriculum and are required of all concentators. The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Core courses may be taken in any order and are required for completion of the concentration. Students may enroll in track courses before they have completed the entire core curriculum.

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Computational Informatics, Information Analysis, Life Science Informatics, or Social Computing. Each of the four tracks requires three to four courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the track.

In addition to the concentration's core and track requirements, students select concentration electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Committee for the concentration will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

A. Computational Informatics Track

Today, computer technology is ubiquitous, and a robust understanding of information systems is important in almost every industry and organization. Computational Informatics emphasizes issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure. In the Computational Informatics track, students learn to assess and build usable software applications for web servers, browsers, smartphones, information analysis tools, and automation of common activities. They develop analytical skills and gain a professional understanding of how people and organizations utilize technology to manage data. Graduates of this track put their skills to use in business and in the financial, software development, and information technology industries. They are also well prepared for graduate programs in computing and information sciences, among others.

B. Data Mining & Information Analysis Track

The collection, analysis, and visualization of complex data play critical roles in research, business, and government. Powerful tools from applied statistics, mathematics, and computational science can be used to uncover the meaning behind complex data sets. The Data Mining and Information Analysis track integrates these disciplines to provide students with practical skills and a theoretical basis for approaching challenging data analysis problems. Students in this track learn how to develop and test models for making predictions, to search through large collections of data for rare and unexpected patterns, and to characterize the degree of certainty associated with discoveries made in the course of data analysis. Skills and knowledge acquired in this track are increasingly important in the job market and are highly relevant for a number of graduate school programs.

C. Life Science Informatics Track

Using artificial information systems, scientists have made great progress in identifying core components of organisms and ecosystems and are beginning to better understand how these components behave and interact with each other. In fact, biology has become an information science, as computational techniques have become an important means to develop and evaluate biological hypotheses. Informatics is used from basic biological research-studying how patterns of gene expression differ across various cell types-to the practice of medicine, where informatics is used to compare treatments, to identify social correlates of health, and to evaluate possible changes in health policy. The Life Science Informatics track prepares students for careers and advanced study in a number of information-related fields in the life sciences, as well as medical school and other areas of graduate study.

D. Social Computing Track

Facebook, Twitter, and shared calendars are now embedded in the fabric of everyday life, but countless other applications have yet to be discovered and perfected, each potentially enhancing the way we interact. Applying knowledge from psychology, economics, and sociology, Social Computing students craft, evaluate, and refine social software computer applications for engaging technology in unique social contexts. Advances in computing have created opportunities for studying patterns of social interaction and developing systems that act as introducers, recommenders, coordinators, and record-keepers. Students in this track develop analytical and problem-solving skills useful in business, software development, and the information industry and are prepared for graduate study in areas including information science, business, and law.

Field of Concentration

For purposes of calculating grade point average, the term "field of concentration" means the following:

  1. All STATS courses.
  2. All courses used to meet concentration requirements.
  3. All mandatory concentration prerequisites.

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

Prerequisites to Core Courses

  1. SI 110 / SOC 110;
  2. MATH 115;
  3. EECS 182 / SI 182;
  4. STATS 250 (or 350) or STATS 400.

Concentration Program

A minimum of 12 courses and 44 credits.

  1. Core: EECS 203, EECS 282, STATS 403 , and SI 410.
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track :
      1. EECS 382
      2. EECS 280
      3. Two of the following Computational courses:
        EECS 281, 376, 476, 477, 481, 484, 485, 492, 493, 494.
      4. Electives*: 8 credits must be elected at the 300-level or higher
    2. Data Mining & Information Analysis track :
      1. STATS 406
      2. STATS 415
      3. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      4. Electives*: 8 credits must be elected at the 300-level or higher
    3. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses:
        • BIOLOGY 305
        • MCDB 310
        • EEB 485
      3. Two of the following Quantitative/Computational courses:
        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 4 credits must be elected at the 300-level or higher
    4. Social Computing track :
      1. PSYCH 280
      2. SI 301
      3. SI 422
      4. SI 429 (or 529)
      5. Electives* 8 credits must be elected at the 300-level or higher
  3. Electives: Additional Informatics electives to bring total concentration credits to 44 credits.The number of electives required for each track varies, depending on the number of required core courses in the track.Informatics concentrators be allowed to select their electives from one of the following lists of courses, depending on their chosen track.Students who wish to use an elective that is not on this list should consult their track advisor before taking the course.  

 

Informatics Pre-Approved Electives

Students may chose electives for their declared track from the following pre-approved lists of electives without consultation of the track advisor.

Note: *Only one elective course in a track indicated with "*" can be taken for elective credit. 

Course Computational Informatics Data Mining & Information Analysis Life Science Informatics Social Computing
BIOINF 463/MATH 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOINF 527 Introduction to Bioinformatics & Computational Biology    Data Mining & Information Analysis *    
BIOINF 545/STATS 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
BIOINF 547/MATH 547/STATS 547 Probabilistic Modeling in Bioinformatics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   
BIOINF 551/BIOLCHEM 551/CHEM 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOMEDE 551/BIOLCHEM 551/CHEM 551/BIOINF 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
BIOPHYS 463/MATH 463/BIOINF 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 449/STATS 449 Topics in Biostatistics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   
BIOSTAT 503 Introduction to Biostatistics        Social Computing *
BIOSTAT 646/BIOINF 545/STATS 545/Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
CHEM 551/BIOLCHEM 551/BIOINF 551/BIOMEDE 551/PATH 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
CMPLXSYS 510/MATH 550 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
EEB 485 Population and Community Ecology      Life Science Informatics *  
EECS 280 Programming and Introductory Data Structures        Social Computing 
EECS 281 Data Structures and Algorithms  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 376 Foundations of Computer Science  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 382 Internet‐scale Computing    Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 476 Theory of Internet Applications  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 477 Introduction to Algorithms  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 481 Software Engineering  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 484 Database Management Systems  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 485 Web Database and Information Systems  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 487 Interactive Computer Graphics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 489 Computer Networks  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 492 Introduction to Artificial Intelligence  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 493 User Interface Development  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
EECS 494 Computer Game Design and Development  Computational Informatics      Social Computing 
EECS 495 Patent Fundamentals for Engineers    Data Mining & Information Analysis * Life Science Informatics * Social Computing 
IOE 310 Introduction to Optimization Methods        Social Computing *
IOE 510/MATH 561/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
IOE 511/MATH 562 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
IOE 512 Dynamic Programming    Data Mining & Information Analysis *   Social Computing *
MATH 416 Theory of Algorithms  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 425/STATS 425 Introduction to Probability  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 433 Introduction to Differential Geometry    Data Mining & Information Analysis     
MATH 451 Advanced Calculus I    Data Mining & Information Analysis  Life Science Informatics   
MATH 462 Mathematical Models    Data Mining & Information Analysis  Life Science Informatics   
MATH 463/BIOINF 463/BIOPHYS 463 Math Modeling in Biology    Data Mining & Information Analysis  Life Science Informatics   
MATH 471 Introduction to Numerical Methods    Data Mining & Information Analysis  Life Science Informatics   
MATH 525/STATS 525 Probability Theory  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 526 Discrete State Stochastic Processes  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
MATH 547/BIOINF 547/STATS 547 Probabilistic Modeling in Bioinformatics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   
MATH 548/STATS 548 Computations in Probabilistic Modeling in Bioinformatics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   
MATH 550/CMPLXSYS 510 Introduction to Adaptive Systems    Data Mining & Information Analysis * Life Science Informatics   
MATH 561/IOE 510/OMS 518 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
MATH 562/IOE 511 Continuous Optimization Methods    Data Mining & Information Analysis *   Social Computing *
MATH 571 Numerical Methods for Scientific Computing I    Data Mining & Information Analysis     
MCDB 408 Genomic Biology    Data Mining & Information Analysis  Life Science Informatics   
MCDB 411 Protein Structure and Function      Life Science Informatics   
OMS 518/IOE 510/MATH 561 Linear Programming I    Data Mining & Information Analysis *   Social Computing *
PATH 551/BIOLCHEM 551/CHEM 551/BIOINF 551/BIOMEDE 551 Proteome Informatics    Data Mining & Information Analysis * Life Science Informatics   
SI 301 Models of Social Information Processing  Computational Informatics  Data Mining & Information Analysis * Life Science Informatics *  
SI 422 Evaluation of Systems and Services  Computational Informatics  Data Mining & Information Analysis * Life Science Informatics *  
SI 429 eCommunities: Analysis & Design of Online Interaction Environments  Computational Informatics       
SI 508 Networks: Theory and Application  Computational Informatics  Data Mining & Information Analysis    Social Computing 
SI 532 Digital Government I: Information Technology and Democratic Politics  Computational Informatics *     Social Computing *
SI 539 Design of Complex Websites  Computational Informatics      Social Computing 
SI 572 Database Design  Computational Informatics  Data Mining & Information Analysis * Life Science Informatics  Social Computing 
SI 583 Recommender Systems  Computational Informatics  Data Mining & Information Analysis *   Social Computing 
SI 631 Practical l Engagement Workshop: Content Management Systems    Data Mining & Information Analysis * Life Science Informatics * Social Computing 
SI 679 Aggregation and Prediction Markets    Data Mining & Information Analysis *   Social Computing 
SI 683 Reputation Systems    Data Mining & Information Analysis *   Social Computing 
SI 689 Computer Supported Cooperative Work  Computational Informatics * Data Mining & Information Analysis * Life Science Informatics * Social Computing *
STATS 401 Applied Statistical Methods II  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 406 Introduction to Statistical Computing  Computational Informatics    Life Science Informatics  Social Computing 
STATS 408 Statistical Principles for Problem Solving: A Systems Approach  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 415 Data Mining  Computational Informatics    Life Science Informatics  Social Computing 
STATS 425/MATH 425 Introduction to Probability  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 426 Introduction to Theoretical Statistics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 430 Applied Probability  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 449/BIOSTAT 449 Topics in Biostatistics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   
STATS 470 Introduction to the Design of Experiments  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 480 Survey Sampling Techniques  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 500 Applied Statistics I  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 525/MATH 525 Probability Theory  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 526/MATH 526 Discrete State Stochastic Processes  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics  Social Computing 
STATS 545/BIOINF 545/BIOSTAT 646 Molecular Genetic and Epigenetic Data    Data Mining & Information Analysis * Life Science Informatics   
STATS 547/MATH 547/BIOINF 547 Probabilistic Modeling in Bioinformatics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   
STATS 548/MATH 548 Computations in Probabilistic Modeling in Bioinformatics  Computational Informatics  Data Mining & Information Analysis  Life Science Informatics   

Honors Concentration

Students interested in doing original research in informatics are encouraged to consider the Informatics Honors Concentration. The Honors concentration is open to all Informatics concentrators who have achieved both a concentration GPA and an overall GPA of 3.4 or better. At least one year prior to graduation, interested students should identify a member of the U-M faculty with informatics expertise to serve as their faculty advisor.  Together with that person, the student prepares a 2-3 paragraph summary of the proposed thesis project, which is submitted together with the Honors Concentration Application to the Informatics Program Coordinator for review by concentration advisors.  The student completes the thesis work in the senior year, while enrolling in 3-4 credits of independent study.  At least six weeks before the last day of classes in the term in which the student will complete the independent study and thesis, an electronic copy of the final Honors thesis is submitted to the Informatics program coordinator. The faculty advisor then solicits comments on the completed thesis from an independent reader, and the student presents the work in a public forum.

 

Informatics concentration (Fall 2010-Fall 2011) +

May be elected as an interdepartmental concentration program

Effective Fall  2010 - Fall 2011

 

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 44 credit hours for completion, including four core courses, 3-4 courses in one of four flexible tracks, plus electives selected from a list of recommended courses.

Four prerequisite courses serve as an introduction to core academic aspects of the curriculum and are required of all concentators. The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Core courses may be taken in any order and are required for completion of the concentration. Students may enroll in track courses before they have completed the entire core curriculum.

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Computational Informatics, Information Analysis, Life Science Informatics, or Social Computing. Each of the four tracks requires three to four courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the track.

In addition to the concentration's core and track requirements, students select concentration electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Committee for the concentration will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

A. Computational Informatics Track

Today, computer technology is ubiquitous, and a robust understanding of information systems is important in almost every industry and organization. Computational Informatics emphasizes issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure. In the Computational Informatics track, students learn to assess and build usable software applications for web servers, browsers, smartphones, information analysis tools, and automation of common activities. They develop analytical skills and gain a professional understanding of how people and organizations utilize technology to manage data. Graduates of this track put their skills to use in business and in the financial, software development, and information technology industries. They are also well prepared for graduate programs in computing and information sciences, among others.

B. Data Mining & Information Analysis Track

The collection, analysis, and visualization of complex data play critical roles in research, business, and government. Powerful tools from applied statistics, mathematics, and computational science can be used to uncover the meaning behind complex data sets. The Data Mining and Information Analysis track integrates these disciplines to provide students with practical skills and a theoretical basis for approaching challenging data analysis problems. Students in this track learn how to develop and test models for making predictions, to search through large collections of data for rare and unexpected patterns, and to characterize the degree of certainty associated with discoveries made in the course of data analysis. Skills and knowledge acquired in this track are increasingly important in the job market and are highly relevant for a number of graduate school programs.

C. Life Science Informatics Track

Using artificial information systems, scientists have made great progress in identifying core components of organisms and ecosystems and are beginning to better understand how these components behave and interact with each other. In fact, biology has become an information science, as computational techniques have become an important means to develop and evaluate biological hypotheses. Informatics is used from basic biological research-studying how patterns of gene expression differ across various cell types-to the practice of medicine, where informatics is used to compare treatments, to identify social correlates of health, and to evaluate possible changes in health policy. The Life Science Informatics track prepares students for careers and advanced study in a number of information-related fields in the life sciences, as well as medical school and other areas of graduate study.

D. Social Computing Track

Facebook, Twitter, and shared calendars are now embedded in the fabric of everyday life, but countless other applications have yet to be discovered and perfected, each potentially enhancing the way we interact. Applying knowledge from psychology, economics, and sociology, Social Computing students craft, evaluate, and refine social software computer applications for engaging technology in unique social contexts. Advances in computing have created opportunities for studying patterns of social interaction and developing systems that act as introducers, recommenders, coordinators, and record-keepers. Students in this track develop analytical and problem-solving skills useful in business, software development, and the information industry and are prepared for graduate study in areas including information science, business, and law.

Field of Concentration

For purposes of calculating grade point average, the term "field of concentration" means the following:

  1. All STATS courses.
  2. All courses used to meet concentration requirements.
  3. All mandatory concentration prerequisites.

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

Prerequisites to Core Courses

  1. SI 110 / SOC 110;
  2. MATH 115;
  3. EECS 182 / SI 182;
  4. STATS 250 (or 350) or STATS 400.

Concentration Program

A minimum of 12 courses and 44 credits.

  1. Core: EECS 203, EECS 282, STATS 403 , and SI 410.
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track :
      1. EECS 382
      2. EECS 280
      3. Two of the following Computational courses:
        EECS 281, 376, 476, 477, 481, 484, 485, 492, 493, 494.
      4. Electives*: 8 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
    2. Data Mining & Information Analysis track :
      1. STATS 406
      2. STATS 415
      3. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      4. Electives*: 8 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
    3. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses:

        • BIOLOGY 305
        • MCDB 310
        • EEB 485
      3. Two of the following Quantitative/Computational courses:

        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 4 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
    4. Social Computing track :
      1. PSYCH 280
      2. SI 301
      3. SI 422
      4. SI 429 (or 529)
      5. Electives* 8 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
  3. Electives : Additional Informatics electives to bring total concentration credits to 44 credits.
    • BIOLCHEM 551
    • BIOMEDE 551
    • BIOINF 527, 545, 547, 551
    • BIOSTAT 449, 503, 553, 646
    • CHEM 551
    • ENGR 410
    • EECS 281, 376, 382, 410, 476, 477, 481, 484, 485, 487, 492, 493, 494, 496, 498, 594, 547
    • MATH 416, 425, 433, 451, 462, 463, 471, 513, 525, 526, 547, 548, 571
    • MCDB 408, 411
    • PATH 551
    • PSYCH 280
    • SI 301, 422, 429 (or 529), 508, 539, 563, 572, 583, 631, 652, 679, 683, 689
    • STATS 401, 406, 408, 415, 425, 426, 430, 449, 470, 480, 500, 525, 526, 545, 547, 548

*Electives must be selected in consultation with a concentration advisor. Alternative courses may be considered for elective credit.

Honors Concentration

Students interested in doing original research in informatics are encouraged to consider the Informatics Honors Concentration. The Honors concentration is open to all Informatics concentrators who have achieved both a concentration GPA and an overall GPA of 3.4 or better. At least one year prior to graduation, interested students should identify a member of the U-M faculty with informatics expertise to serve as their faculty advisor.  Together with that person, the student prepares a 2-3 paragraph summary of the proposed thesis project, which is submitted together with the Honors Concentration Application to the Informatics Program Coordinator for review by concentration advisors.  The student completes the thesis work in the senior year, while enrolling in 3-4 credits of independent study.  At least six weeks before the last day of classes in the term in which the student will complete the independent study and thesis, an electronic copy of the final Honors thesis is submitted to the Informatics program coordinator. The faculty advisor then solicits comments on the completed thesis from an independent reader, and the student presents the work in a public forum.

 

Informatics concentration (Winter 2010-Summer 2010) +

 

May be elected as an interdepartmental concentration program

Effective Winter 2010-Summer 2010 

 

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 44 credit hours for completion, including core courses, concentration track requirements, and electives. The concentration consists of four courses, 3-4 courses in one of four flexible tracks, plus electives selected from a list of recommended courses.

The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. Each core course carries one prerequisite course that serves as an introduction to some of the core academic aspects of the associated core course. Discrete Mathematics (EECS/MATH 203) requires entry-level calculus (MATH 115) or an equivalent. Computer Programs and Models (EECS 282) requires an exploratory course on building computer applications (EEC 182/SI 182 or an equivalent). Introduction to Quantitative Research Methods (STATS 403) builds on the basic principles of statistics and data analysis introduced in a popular survey course (STATS 250 [or 350] or an equivalent). Advanced study of ethics and information technology (SI 410) follows the exploration of the broader issues of information studies (SI 110). The core courses can be taken in any order and are required for completion of the concentration. Students may enroll in courses for the individual program tracks before they have completed the entire core curriculum.

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Computational Informatics, Information Analysis, Life Science Informatics, or Social Computing. Each of the four tracks requires three to five courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen courses that together convey the necessary intellectual perspectives and foundational skills of the informatics track.

In addition to the concentration's core and track requirements, students will choose concentration electives from a list of recommended  courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Group for the concentration will entertain appeals from students to substitute elective courses other than those in the list of recommended electives.

A. Computational Informatics Track

In the 21st century, computing and information are ubiquitous and an understanding of computer technology and information systems is important for students with a wide variety of long-term goals-not just those who want to go into the more traditional sectors of the computing industry. The Computational Informatics track provides students with a solid technical foundation in computing and in the ways in which information is represented and manipulated by computers to solve complex and large-scale problems in a variety of domains. It complements this with a broad focus on information technology assessment and evaluation and on the factors affecting the "usability" of systems by individuals and organizations. The track emphasizes the issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure.

B. Data Mining & Information Analysis Track

Massive amounts of data are now being collected routinely in many areas such as business, public health, science, and engineering. Just as students in the humanities learn to think critically about texts, bodies of literature, and verbal arguments, students in the sciences and engineering must learn about the important issues with collecting, managing, analyzing, and visualizing data. Such a requirement was not so important several decades ago. Now, with the explosion in computing and data capture technologies, expertise in dealing with large datasets is increasingly important in the job market and for graduate school in many disciplines. No single academic program can provide students with expertise in collecting, evaluating, managing, analyzing, visualizing, and making sense of these data and using the information to make decisions in the presence of varying degrees of uncertainty. The fields of Statistics, Computer Science, Information, and Mathematics all play fundamental roles in this educational process as they provide the tools, language, the modes of critical thought, and the rigorous analytical methods.

C. Life Science Informatics Track

The advent of superior data collection systems and high throughput experimental methods have resulted in a deluge of data for a broad range of life science disciplines. It is not hard to find a life scientist making statements such as "biology has now become an information science," as computational techniques have become an important means to developing and evaluating biological hypotheses. This track prepares students fur careers and/or advanced study in the life sciences, including medical school, by providing them with an understanding of both the fundamentals of biology and the principles of computation and statistics that are key to a great deal of life sciences research

D. Social Computing Track

In the Social Computing track, undergraduates will learn a set of perspectives and analytic techniques, derived from the core social science disciplines of cognitive psychology, economics, and sociology, that will allow them to craft, evaluate and refine social software computer applications to meet the needs of different social contexts. Advances in computing have opened new opportunities both for understanding patterns of social interaction and for supporting new patterns. As more interactions are mediated through computers and computer networks, there are new opportunities to capture and analyze data about those interactions. Formal representations such as social network graphs of who communicates with whom and game-theoretic models of strategic choices individuals can make when interacting with each other can offer useful insights. Computation can also support new forms of social relations. Systems can facilitate remote and asynchronous communication, and act as introducers, recommenders, coordinators, and record-keepers. Applications such as email groups, buddy lists, and shared calendars are now embedded into the fabric of everyday life, but countless more such applications remain to be discovered and perfected.

Field of Concentration.

For purposes of calculating grade point average, the term "field of concentration" means the following:

  1. All STATS courses.
  2. All courses used to meet concentration requirements.
  3. All mandatory prerequisites (to the core courses).

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

Prerequisites to Core Courses:

  1. SI 110 / SOC 110;
  2. MATH 115;
  3. EECS 182 / SI 182;
  4. STATS 250 (or 350) or STATS 400.

Concentration Program.

A minimum of 12 courses and 44 credits.

  1. Common Core: EECS 203, EECS 282, STATS 403 , and SI 410.
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track :
      1. EECS 382
      2. EECS 280
      3. Two of the following Computational courses:
        EECS 281, 376, 476, 477, 481, 484, 485, 492, 493, 494.
      4. Electives*: 8 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
    2. Data Mining & Information Analysis track :
      1. STATS 406
      2. STATS 415
      3. One of the following Quantitative courses:
        • MATH 425, 471, 561, 562, 571
        • STATS 425, 500
        • IOE 310, 510, 511, 512
      4. Electives*: 8 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
    3. Life Science Informatics track :
      1. BIOINF 527
      2. One of the following Life Sciences courses

        • BIOLOGY 305
        • MCDB 310
        • EEB 485
      3. Two of the following Quantitative/Computational courses:

        • EECS 376, 382, 485
        • STATS 401, 449, 470
        • BIOSTAT 449
      4. Electives*: 4 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
    4. Social Computing track :
      1. PSYCH 280
      2. SI 301
      3. SI 422
      4. SI 429 (or 529)
      5. Electives* 8 credits must be elected at the 300-level or higher, and all electives must be selected in consultation with a faculty advisor. 
  3. Electives : Additional Informatics electives to bring total concentration credits to 44 credits.
    • BIOLCHEM 551
    • BIOMEDE 551
    • BIOINF 527, 545, 547, 551
    • BIOSTAT 449, 503, 553, 646
    • CHEM 551
    • CMPLXSYS 510
    • EECS 281, 376, 382, 476, 477, 481, 484, 485, 487, 489, 492, 493, 494, 495, 496
    • MATH 416, 425, 433, 451, 462, 463, 471, 513, 525, 526, 547, 548, 550
    • MCDB 408, 411
    • PATH 551
    • PSYCH 280
    • SI 301, 422, 429 (or 529), 508, 532, 583, 631, 679, 683, 689
    • STATS 401, 406, 408, 415, 425, 426, 430, 449, 470, 480, 500, 525, 526, 545, 547, 548

Electives must be selected in consultation with a concentration advisor. Alternative courses may be considered for elective credit.

 

Informatics concentration (Winter 2009-Fall 2009) +

 

May be elected as an interdepartmental concentration program

Effective Winter 2009 - Fall 2009  

 

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 44 credit hours for completion, including core courses, concentration track requirements, and electives. The concentration consists of a core curriculum of four tightly integrated and often team-taught courses, a sequence of required courses in one of four flexible program tracks, plus electives selected from a list of recommended courses.

The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. There are no overall prerequisites for the concentration. Instead, each core course carries one prerequisite course that serves as an introduction to some of the core academic aspects of the associated core course. Discrete Mathematics (EECS/MATH 203) requires entry-level calculus (MATH 115) or an equivalent. Computer Programs and Models (EECS 282) requires an exploratory course on building computer applications (EEC 182/SI 182). Introduction to Quantitative Research Methods (STATS 403) builds on the basic principles of statistics and data analysis introduced in a popular survey course (STATS 350). Advanced study of ethics and information technology (SI 410) follows the exploration of the broader issues of information studies (SI 110). The core courses can be taken in any order and are required for completion of the concentration. Students may enroll in courses for the individual program tracks before they have completed the entire core curriculum.

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Information Analysis, Social Computing, Computational Informatics, or Life Science Informatics. Each of the four tracks requires three to five courses, some of which will have associated prerequisite courses enforced at registration. The tracks consist of a set of carefully chosen existing or new courses from multiple departments that together convey the necessary intellectual perspectives and foundational skills of the informatics track.

In addition to the concentration's core and track requirements, students will choose from a list of Informatics elective courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Group for the concentration will entertain appeals from students to substitute elective courses other than those listed in the proposal

A. Computational Informatics Track

In the 21st century, computing and information are ubiquitous and an understanding of computer technology and information systems is important for students with a wide variety of long-term goals-not just those who want to go into the more traditional sectors of the computing industry. The Computational Informatics track provides students with a solid technical foundation in computing and in the ways in which information is represented and manipulated by computers to solve complex and large-scale problems in a variety of domains. It complements this with a broad focus on information technology assessment and evaluation and on the factors affecting the "usability" of systems by individuals and organizations. The track emphasizes the issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure.

B. Data Mining & Information Analysis Track

Massive amounts of data are now being collected routinely in many areas such as business, public health, science, and engineering. Just as students in the humanities learn to think critically about texts, bodies of literature, and verbal arguments, students in the sciences and engineering must learn about the important issues with collecting, managing, analyzing, and visualizing data. Such a requirement was not so important several decades ago. Now, with the explosion in computing and data capture technologies, expertise in dealing with large datasets is increasingly important in the job market and for graduate school in many disciplines. No single academic program can provide students with expertise in collecting, evaluating, managing, analyzing, visualizing, and making sense of these data and using the information to make decisions in the presence of varying degrees of uncertainty. The fields of Statistics, Computer Science, Information, and Mathematics all play fundamental roles in this educational process as they provide the tools, language, the modes of critical thought, and the rigorous analytical methods.

C. Life Science Informatics Track

The advent of superior data collection systems and high throughput experimental methods have resulted in a deluge of data for a broad range of life science disciplines. It is not hard to find a life scientist making statements such as "biology has now become an information science," as computational techniques have become an important means to developing and evaluating biological hypotheses. This track prepares students fur careers and/or advanced study in the life sciences, including medical school, by providing them with an understanding of both the fundamentals of biology and the principles of computation and statistics that are key to a great deal of life sciences research

D. Social Computing Track

In the Social Computing track, undergraduates will learn a set of perspectives and analytic techniques, derived from the core social science disciplines of cognitive psychology, economics, and sociology, that will allow them to craft, evaluate and refine social software computer applications to meet the needs of different social contexts. Advances in computing have opened new opportunities both for understanding patterns of social interaction and for supporting new patterns. As more interactions are mediated through computers and computer networks, there are new opportunities to capture and analyze data about those interactions. Formal representations such as social network graphs of who communicates with whom and game-theoretic models of strategic choices individuals can make when interacting with each other can offer useful insights. Computation can also support new forms of social relations. Systems can facilitate remote and asynchronous communication, and act as introducers, recommenders, coordinators, and record-keepers. Applications such as email groups, buddy lists, and shared calendars are now embedded into the fabric of everyday life, but countless more such applications remain to be discovered and perfected.

Field of Concentration.

For purposes of calculating grade point average, the term "field of concentration" means the following:

  1. All STATS courses.
  2. All courses used to meet concentration requirements.
  3. All mandatory prerequisites (Prerequisites to the Core Courses).

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

Prerequisites to Core Courses:

  1. SI 110 / SOC 110;
  2. MATH 115;
  3. EECS 182 / SI 182;
  4. STATS 350 or STATS 400.

Concentration Program.

A minimum of 12 courses and 44 credits.

  1. Common Core: EECS 203, EECS 282, STATS 403 , and SI 410.
  2. Subplans: Completion of one of the following tracks:
    1. Computational Informatics track [4 required courses for 13 credits]:
      1. EECS 382.
      2. Computational Foundations: one of EECS 281, 376, 476, 477, 492.
      3. Database Systems: one of EECS 484 or 485.
      4. Human-Computer Interaction: one of SI 422, EECS 481, 493, 494.
    2. Data Mining & Information Analysis track [3 required courses for 11 credits]:
      MATH 471; STATS 406; and STATS 415.
    3. Life Science Informatics track [5 required courses for 17-19 credits]:
      1. BIOLOGY 305 .
      2. MCDB 310 [or MCDB 311 or BIOLCHEM 415 or BIOLCHEM 451 or CHEM 451] .
      3. BIOINF 525 / 527 .
      4. Life Sciences: one of MCDB 427, MCDB 428, EEB 485 , CHEM 452 or BIOLCHEM 452.
      5. Quantitative/Computational: one of EECS 382, EECS 376, STATS 401, STATS 449 or BIOSTAT 449.
    4. Social Computing track [4 required courses for 15-16 credits]:
      PSYCH 280; SI 301; SI 422; and SI 529.
  3. Electives : Additional Informatics electives to bring concentration credits to 44 credits.
    • BIOLOGY 305
    • BIOLCHEM 415, 451, 452, 551
    • BIOMEDE 551
    • BIOINF 527 , 545, 547, 551
    • BIOSTAT 449, 503, 553, 646
    • CHEM 451, 452, 551
    • CMPLXSYS 510
    • EEB 485
    • EECS 281, 376, 382, 476, 477, 481, 484, 485, 487, 489, 492, 493, 494, 495, 496
    • MATH 416, 425, 433, 451, 462, 463, 471, 513, 525, 526, 547, 548, 550
    • MCDB 310, 311, 408, 411, 427, 428
    • PATH 551
    • PSYCH 280
    • SI 301, 422, 508, 529, 532, 583, 631, 679, 683, 689
    • STATS 401, 406, 408, 415, 425, 426, 430, 449, 470, 480, 500, 525, 526, 545, 547, 548

Electives must be selected in consultation with a concentration advisor. Alternative courses may be considered for elective credit.

 

Informatics concentration (Fall 2008) +

 

May be elected as an interdepartmental concentration program

Effective Fall 2008 

Informatics concentrators may not use any STATS courses toward the Area Distribution requirement.

 

Summary of Course Requirements and Prerequisites

The concentration in Informatics requires 44 credit hours for completion, including core courses, concentration track requirements, and electives. The concentration consists of a core curriculum of four tightly integrated and often team-taught courses (15 credits), a sequence of required courses in one of four flexible program tracks (between 11 and 16 credits), plus electives selected from a list of recommended courses (between 13 and 18 credits, depending upon track). The core serves as a tour of critical perspectives and investigative methodologies, an introduction to tools and techniques, and an entry point for further study. The four core courses provide grounding in discrete mathematics, computer programs and models, research methods in applied statistics, and the ethical issues posed by new and emerging technologies. Each of the four core courses helps establish a foundation for the advanced study of informatics issues pursued through the specific informatics tracks. The tracks consist of a set of carefully chosen existing or new courses from multiple departments that together convey the necessary intellectual perspectives and foundational skills of the informatics track. The core courses can be taken in any order and are required for completion of the concentration. Students may enroll in courses for the individual program tracks before they have completed the entire core curriculum.

There are no overall prerequisites for the concentration. Instead, each core course carries one prerequisite course that serves as an introduction to some of the core academic aspects of the associated core course. Discrete mathematics (EECS/MATH 203) requires entry-level calculus (MATH 115) or an equivalent. Computer programs and models (EECS 282) requires an exploratory course on building computer applications (EEC 182/SI 182). Statistical research methods (STATS 410) builds on the basic principles of statistics and data analysis introduced in a popular survey course (STATS 350). Advanced study of ethics and information technology (SI 410) follows from the exploration of the broader issues of information studies (SI 110).

In pursuing the concentration in Informatics, students have the flexibility to specialize in one of four tracks: Information Analysis, Social Computing, Computational Informatics, or Life Science Informatics. Each of the four tracks requires three or four courses, some of which will have associated prerequisite courses enforced at registration.

In addition to the concentration's core and track requirements, students will choose from a list of Informatics elective courses. The breadth of electives will allow students to add intellectual depth to their selected track studies or to broaden their perspective on other aspects of the informatics field. The Faculty Steering Group for the concentration will entertain appeals from students to substitute elective courses other than those listed in the proposal

A. Information Analysis Track [3 required courses for 11 credits]. Massive amounts of data are now being collected routinely in many areas such as business, public health, science, and engineering. Just as students in the humanities learn to think critically about texts, bodies of literature, and verbal arguments, students in the sciences and engineering must learn about the important issues with collecting, managing, analyzing, and visualizing data. Such a requirement was not so important several decades ago. Now, with the explosion in computing and data capture technologies, expertise in dealing with large datasets is increasingly important in the job market and for graduate school in many disciplines. No single academic program can provide students with expertise in collecting, evaluating, managing, analyzing, visualizing, and making sense of these data and using the information to make decisions in the presence of varying degrees of uncertainty. The fields of Statistics, Computer Science, Information, and Mathematics all play fundamental roles in this educational process as they provide the tools, language, the modes of critical thought, and the rigorous analytical methods.

B. Social Computing Track [4 required courses for 13 credits]

In the Social Computing track, undergraduates will learn a set of perspectives and analytic techniques, derived from the core social science disciplines of cognitive psychology, economics, and sociology, that will allow them to craft, evaluate and refine social software computer applications to meet the needs of different social contexts. Advances in computing have opened new opportunities both for understanding patterns of social interaction and for supporting new patterns. As more interactions are mediated through computers and computer networks, there are new opportunities to capture and analyze data about those interactions. Formal representations such as social network graphs of who communicates with whom and game-theoretic models of strategic choices individuals can make when interacting with each other can offer useful insights. Computation can also support new forms of social relations. Systems can facilitate remote and asynchronous communication, and act as introducers, recommenders, coordinators, and record-keepers. Applications such as email groups, buddy lists, and shared calendars are now embedded into the fabric of everyday life, but countless more such applications remain to be discovered and perfected.

C. Computational Informatics Track [4 required courses for 15-16 credits]. In the 21'' century, computing and information are ubiquitous and an understanding of computer technology and information systems is important for students with a wide variety of long-term goals-not just those who want to go into the more traditional sectors of the computing industry. The Computational Informatics track provides students with a solid technical foundation in computing and in the ways in which information is represented and manipulated by computers to solve complex and large-scale problems in a variety of domains. It complements this with a broad focus on information technology assessment and evaluation and on the factors affecting the "usability" of systems by individuals and organizations. The track emphasizes the issues involved in the design of computing solutions, rather than focusing on the underlying computing infrastructure.

D. Life Science Informatics Track [5 required courses for 19-20 credits]. The advent of superior data collection systems and high throughput experimental methods have resulted in a deluge of data for a broad range of life science disciplines. It is not hard to find a life scientist making statements such as "biology has now become an information science," as computational techniques have become an important means to developing and evaluating biological hypotheses. This track prepares students fur careers and/or advanced study in the life sciences, including medical school, by providing them with an understanding of both the fundamentals of biology and the principles of computation and statistics that are key to a great deal of life sciences research

Prerequisites to Core Courses:

  • SI 110 / SOC 110;
  • MATH 115;
  • EECS 182 / SI 182;
  • STATS 350 or STATS 400.

Concentration Program . A minimum of 12 courses and 44 credits.

  1. Common Core: EECS 203, EECS 282, STATS 410, and SI 410.
  2. Subplans: Completion of one of the following tracks:
    1. Information Analysis track: MATH 471; STATS 406; and STATS 415.
    2. Social Computing track: PSYCH 280; SI 301; SI 422; and SI 584.
    3. Computational Informatics track:
      1. EECS 382.
      2. Computational Foundations: one of EECS 281, 376, 476, 477, 492.
      3. Database Systems: one of EECS 484 or 485.
      4. Human-Computer Interaction: one of SI 422, EECS 481, 493, 494.
    4. Life Science Informatics track:
      1. Genetics: MCDB 305.
      2. Biochemistry: MCDB 310 [or 311 or 311 or BIOLCHEM 415 or CHEM 451 or BIOLCHEM 451].
      3. BIOINF 527.
      4. Life Sciences: One of MCDB 427, MCDB 428, EEB 485, CHEM 452 or BIOLCHEM 452.
      5. Quantitative/Computational course: One of EECS 382, EECS 376, STATS 401, STATS 449 or BIOSTAT 449.
  3. Electives: Additional Informatics electives to bring concentration credits to 44 credits.
    • BIOLCHEM 415, 451, 452, 551
    • BIOMEDE 551
    • BIOINF 527, 545, 547, 551
    • BIOSTAT 449, 503, 553, 646
    • CHEM 451, 452, 551
    • CMPLXSYS 510
    • EEB 485
    • EECS 281, 376, 382, 476, 477, 481, 484, 485, 487, 489, 492, 493, 494, 495, 496
    • MATH 416, 425, 433, 451, 462, 463, 471, 513, 525, 526, 547, 548, 550
    • MCDB 408, 411, 427, 428
    • PATH 551
    • SI 310, 410, 422, 508, 532, 583, 584, 631, 679, 683, 689
    • STATS 401, 406, 408, 415, 425, 426, 430, 449, 470, 480, 500, 525, 526, 545, 547, 548

 


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