### Home / Curriculum /

# Data Mining & Information Analysis

*Due to the creation of the new Data Science undergraduate program, the Data Mining and Information Analysis track of the Informatics program will not accept declarations from new majors after July 1, 2015. Students interested in Data Mining should consider majoring in Data Science instead. Students who have already declared Informatics Data Mining as their major will be able to complete the program as currently defined. More information about Data Science is currently available from the Informatics program advisors. A web site for Data Science will be launched during the winter semester of 2015.*

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.

All Data Mining & Information Analysis students who declared the major in Informatics between September 2008 and December 2009 may follow the original curriculum or the curriculum outlined for students who declared after January 1, 2010. Students who declare January 2013 or after will follow the curriculum below. Please contact the Program Coordinator for questions.

## Track Courses (15-16 credits)

The following courses:

### MATH 217 Linear Algebra +

### STATS 406 Introduction to Statistical Computing +

### STATS 415 Data Mining and Statistical Learning +

## One of the following courses:

### MATH 471 Introduction to Numerical Methods +

### MATH 571 Numerical Methods for Scientific Computing I +

### MATH / STATS 425 Introduction to Probability +

### STATS 500 Applied Statistics I +

### IOE 310 Introduction to Optimization Methods +

### IOE 510 / MATH 561 Linear Programming I +

### IOE 511 / MATH 562 Continuous Optimization Methods +

### IOE 512 Dynamic Programming +

** Courses have been historically offered as indicated (F = Fall, W = Winter, Sp = Spring, Su = Summer). Terms in which courses are offered are, however, subject to change.*

Note: Students may enroll in track courses prior to completing all prerequisite and core courses.

Use this spreadsheet to calculate a concentration GPA in Informatics with a Data Mining and Information Analysis track. Use all attempts at a course in the GPA calculation.

## Elective Courses (12-13 credits)

Eight [8] elective credits must be at the 300 level or higher. See the list of approved concentration electives.

In consultation with a faculty advisor, a course not on the approved list of electives may be selected to fulfill elective credit. Approval of the course must be obtained prior to enrollment. The Informatics Elective Approval Form must also be submitted to the Program Coordinator in 439 West Hall.