*
Effective Winter 2014
*

### Advising

The Academic Program Manager and members of the Faculty Steering Committee that designed the major share responsibility for major advising. Students who are interested in the Informatics major should consult with an Academic Advisor in the Newnan LSA Academic Advising Center during their freshman year and are strongly encouraged to meet with a department advisor early in their academic career. To make an appointment with a department advisor, please contact informatics@umich.edu.

### Grade Policies

**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.

### Prerequisites

**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 or 280 with a C or better.

**Prerequisite to Declaration**

MATH 115, STATS 250 or 280, and EECS 182 or 183.

### Requirements

**Minimum Credits:**40

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

EECS 203, EECS 280, STATS 403*Core:*Completion of one of the following tracks:*Subplans:***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:***(Note: inactive as of Spring 2017)*- 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

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.*Electives:*

**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.

**Life Science Informatics Track**

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

**Data Mining & Information Analysis Track (Note: Inactive as of Spring 2017)**

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

### Constraints

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

### Distribution Policy

No course used to fulfill a major requirement may be used toward the LSA Distribution Requirement. In addition, courses in the STATS subject area may not be used toward the Distribution Requirement.### Honors

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 three 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.

**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.**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.Note: This track is scheduled to be phased out in the near future and be replaced by the Internet Informatics Track.*(At the end of Fall 2013 declaration to this track will be discontinued)*

**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.

**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.*(this track will be phased out in Fall 2013; students considering Social Computing beyond this date should apply for the B.S. in Information)*

**Field of Major**

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:- Computational Informatics track :
- EECS 382
- Two of the following Computational/Quantitative courses: EECS 281 and one of 376, 388, 476, 477, 481, 484, 485, 492, 493, 494.
*Electives*:*8 credits must be elected at the 300-level or higher

- 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

- Social Computing track :
- PSYCH 280
- SI 301
- SI 422
- SI 429 (or 529)
*Electives**8 credits must be elected at the 300-level or higher

- Computational Informatics track :
*Electives:*Additional Informatics electives to bring total major credits to 40 credits. 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 major.*

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 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.

**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.**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.Note: This track is scheduled to be phased out in the near future and be replaced by the Internet Informatics Track.*(At the end of Fall 2013 declaration to this track will be discontinued)*

**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.**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.

**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.*(this track will be phased out in Fall 2013; students considering Social Computing beyond this date should apply for the B.S. in Information)*

**Field of Major**

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 282 or 280*, STATS 403

*If a student takes both EECS 282 and 280, EECS 280 will be treated as an elective.*Subplans:*Completion of one of the following tracks:- Computational Informatics track :
- EECS 280 (note: students who did not take EECS 282 will need to take an additional 4 credits of electives)
- EECS 382
- Two of the following Computational/Quantitative courses: EECS 281 and one of 376, 388, 476, 477, 481, 484, 485, 492, 493, 494.
*Electives*:*8 credits must be elected at the 300-level or higher

- 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

- Social Computing track :
- PSYCH 280
- SI 301
- SI 422
- SI 429 (or 529)
*Electives**8 credits must be elected at the 300-level or higher

- Computational Informatics track :
*Electives:*Additional Informatics electives to bring total major credits to 40 credits. 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 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 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 2012)

*May be elected as an interdepartmental major.*

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 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 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 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.

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 major 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 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.

**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.

**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.**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.**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.

**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 Major**

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.

**Prerequisites to Core Courses**

- SI 110 / SOC 110;
- MATH 115;
- EECS 182 / SI 182;
- STATS 250 (or 350) or STATS 400.

**Requirements for the Major**

A minimum of 12 courses and 44 credits.

*Core:*EECS 203, EECS 282, STATS 403 , and SI 410.*Subplans:*Completion of one of the following tracks:- Computational Informatics track :
*EECS 382**EECS 280*- Two of the following Computational/Quantitative courses:

EECS 281, 376, 476, 477, 481, 484, 485, 492, 493, 494. *Electives*:*8 credits must be elected at the 300-level or higher

- Data Mining & Information Analysis track :
- 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

- Internet Informatics track
- EECS 382
- EECS 485
- EECS 398, section titled
*"Information Security"* *Four wide technical electives (16 credits)*

- 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

- Social Computing track :
- PSYCH 280
- SI 301
- SI 422
- SI 429 (or 529)
*Electives**8 credits must be elected at the 300-level or higher

- Computational Informatics track :
*Electives:*Additional Informatics electives to bring total major 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 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**

** 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 |