The Department of Statistics is also a partner in the interdisciplinary major in Informatics. Michigan's interdisciplinary approach to teaching Informatics gives you a solid grounding in contemporary computer programming, mathematics, and statistics, combined with study of the ethical and social science aspects of complex information systems.
Click on the button below for students still on the following tracks (effective Winter 2013):
The Life Science Informatics track is the remaining track available to declare in the Informatics major. Students who have previously declared another track will be able to complete what they have declared.
- Computational Informatics (no longer available to declare, students should consider the Computer Science -LSA major)
- Data Mining Information Analysis (no longer available to declare, students should consider the Data Science - LSA major)
- Life Science Informatics
- Social Computing (no longer available to declare, students should consider the Bachelor of Science in Information program through the School of Information)
Click on the button below for students still on the following tracks (effective Fall 2014):
- Data Mining Information Analysis
- Life Sciences
Life Sciences Informatics
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.
All Life Science Informatics students who declared the major in Informatics between September 2008 and December 2009 may follow the original curriculum or the new curriculum outlined below. If choosing to follow the new curriculum, please notify the Program Coordinator.
All Life Science Informatics who declare after January 1, 2010 will follow the curriculum outlined below:
- SI/UC 110: Intoduction to Information Studies (4 credits)
This course will provide the foundational knowledge necessary to begin to address the key issues associated with the Information Revolution. Issues will range from the theoretical (what is information and how do humans construct it?) to the cultural (is life on the screen a qualitatively different phenomenon from experiences with earlier distance-shrinking and knowledge-building technologies such as telephones?) to the practical (what are the basic architectures of computing and networks?). Successful completion of this “gateway” course will provide the student with the conceptual tools necessary to understand the politics, economics, and culture of the Information age, providing a foundation for later study in Information or any number of more traditional disciplines.
- MATH 115: Calculus I (or equivalent) (4 credits)
The course presents the concepts of calculus from four points of view: geometric (graphs), numeric (tables), symbolic (formulas), and verbal descriptions. Students will develop their reading, writing, and questioning skills, as well as their ability to work cooperatively. Topics include functions and graphs, derivatives and their applications to real-life problems in various fields, and an introduction to integration. The classroom atmosphere is interactive and cooperative. Both individual and team homework is assigned.
- EECS 183: Elementary Programming Concepts (or equivalent) (4 credits)
Fundamental concepts and skills of programming in a high-level language. Flow of control: selection, iteration, subprograms. Data structures: strings, arrays, records, lists, tables. Algorithms using selection and iteration (decision making, finding maxima/minima, searching, sorting, simulation, etc.). Good program design, structure and style are emphasized. Testing and debugging.
This course (website: eecs183.org) is an introductory course to computer science and programming. Students will learn the basics of computing, as well as problem-solving and algorithmic thinking. Languages include C++ and Python.
- STATS 250: Introduction to Statistics and Data Analysis (or equivalent) (4 credits)
In this course students are introduced to the concepts and applications of statistical methods and data analysis. Examples of applications are drawn from virtually all academic areas and some attention is given to statistical process control methods.
- EECS 203: Discrete Math (4 credits)
Introduction to the mathematical foundations of computer science.
- EECS 280: Programming & Introductory Data Structures
Techniques and algorithm development and effective programming, top-down analysis, structured programming, testing, and program correctness. Program language syntax and static and runtime semantics. Scope, procedure instantiation, recursion, abstract data types, and parameter passing methods. Structured data types, pointers, linked data structures, stacks, queues, arrays, records, and trees.
- STATS 403: Introduction to Quantitative Research Methods
This course provides an introduction to quantitative methods for research studies. Issues of study design, implementation, data analysis, and interpretation and communication of results are covered. Using examples from biomedicine! social science! public policy, business, and environmental science, students will learn how statistical methods are used by researchers to plan effective studies, and to aid in drawing meaningful conclusions from their data. Major themes of the course will include confounding and causality, heterogeneity! generalization, estimation bias and precision, and power. Students will learn how to evaluate the performance of statistical procedures, and what it means for a statistical procedure to depend on "assumptions." Research papers from various fields will be used to illustrate the methods and principles taught in the course.
Track Courses (14-15 credits)
BIOINF 527 Introduction to Bioinformatics and Computational Biology
This course introduces students to the fundamental theories and practices of Bioinformatics and Computational Biology via a series of integrated lectures and labs. These lectures and labs will focus on the basic knowledge required in this field, methods of high-throughput data generation, accessing public genome-related information and data, and tools for data mining and analysis. The course is divided into four areas: Basics of Bioinformatics, Computational Phylogeny (includes sequence analysis), Systems Biology and Modeling.
Advisory prerequisites: Upper level or graduate level Statistics or concurrent enrollment in Statistics; Calculus I & II; Biochemistry, Molecular Biology, or Cellular biology; or permission of instructor.
4 credits. Offered F
One of the following life science courses:
This introduction to genetics includes the following sections: DNA and chromosomes; gene transmission in Eukaryotes; linkage and recombination; genes and enzymes, the genetic code, and mutation; recombinant DNA, RFLP mapping, the Human Genome Project; gene regulation, transposons; population genetics; and quantitative genetics.
Enforced prerequisites: BIOLOGY 162 or 163 or (171 and (172 or 174)) or 195. Prior or concurrent enrollment in CHEM 210.
3 credits. Offered: F, W, Sp.
MCDB 310 Introductory Biochemistry
Introductory Biochemistry is designed to be a general introduction to the chemistry of biological systems. The biweekly lectures for this course are designed to help students put biochemical reactions into a cellular context. Students are exposed to the strategies used by cells and multicellular organisms to coordinate the activity of various metabolic pathways.
Enforced prerequisites: BIOLOGY 162 or 163 or 172 or 174 or (195 & 173) and CHEM 210.
Advisory prerequisite: Prior or concurrent enrollment in CHEM 215.
Two of the following computational/quantitative courses:
EECS 376 Foundations of Computer Science
An introduction to computation theory: finite automata, regular languages, pushdown automata, context-free languages, Turing machines, recursive languages and functions, and computational complexity.
Enforced prerequisites: EECS 203 or 303 or CMPTRSC 203 or 303 with a grade of C or better; and EECS 280 or CMPTRSC 280 with a grade of C or better.
4 credits. Offered F, W
EECS 485 Web Database and Information Systems
Concepts surrounding Web information system, including client/server systems, security, XML, information retrieval and search engines, and data replication issues. Includes substantial final project involving development of a database backed web site.
Enforced prerequisite: EECS 281 or 382 with a grade of C or better.
4 credits. Offered F
STATS 401 Applied Statistical Methods II
An intermediate course in applied statistics which assumes knowledge of STAT 350/400-level material. Covers a range of topics in modeling and analysis of data including: review of simple linear regression, two-sample problems, one-way analysis of variance; multiple linear regression, diagnostics and model selection; two-way analysis of variance, multiple comparisons, and other selected topics.
Advisory prerequisites: MATH 115; and STATS 250, STATS 400, STATS 405, ECON 405, or NRE 438. No credit granted to those who have completed or are enrolled in STATS 413.
4 credits. Offered F, W
STATS / BIOSTAT 449 Topics in Biostatistics
Introduction to biostatistical topics: clinical trials, cohort and case-control studies; experimental versus observational date; issues of causation, randomization, placebos; case control studies; survival analysis; diagnostic testing; image analysis of PET and MRI scans; statistical genetics; longitudinal studies; and missing data.
Advisory prerequisite: STATS 401 or permission of instructor.
3 credits. Offered W
STATS 470 Introduction to the Design of Experiments
Introduces students to basic concepts for planning experiments and to efficient methods of design and analysis. Topics covered include concepts such as randomization, replication and blocking; analysis of variance and covariance and the general linear model; factorial and fractional factorial designs, blocked designs, and split-plot designs.
Enforced prerequisite: STATS 401, STATS 412, STATS 425, or MATH 425.
4 credits. Offered F
*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 major GPA in Informatics with a Life Science Informatics track. Use all attempts at a course in the GPA calculation.
Elective Courses (13-14 credits)
Four  elective credits must be at the 300 level or higher. See the list of approved major 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.