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# Informatics Major

*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 three 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.

**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.**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:

- All STATS courses.
- All courses used to meet requirements for the major.
- 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**

- SI 110 / SOC 110 with a C or better;
- MATH 115 with a C or better;
- EECS 182 / SI 182 or EECS 183 with a C or better;
- 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.

*Core:*EECS 203, EECS 280, STATS 403*Subplans:*Completion of one of the following tracks:- Data Mining & Information Analysis track :
- MATH 217
- STATS 406
- STATS 415
- One of the following Quantitative courses:
- MATH 425, 471, 561, 562, 571
- STATS 425, 500
- IOE 310, 510, 511, 512

*Electives*:*8 credits must be elected at the 300-level or higher

- Life Science Informatics track :
- BIOINF 527
- One of the following Life Sciences courses:
- BIOLOGY 305
- MCDB 310

- Two of the following Quantitative/Computational courses:
- EECS 376, 382, 485
- STATS 401, 449, 470
- BIOSTAT 449

*Electives*:*12-14 credits; 4 credits must be elected at the 300-level or higher

- Data Mining & Information Analysis track :
*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 Internetāscale 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.

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