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Applied Data Science Master's
Degree Requirements for the M.S. in Applied Data Science

Degree Requirements for the M.S. in Applied Data Science

Degree Requirements

The plan of study is 30 credit hours. It includes six core courses and four specialization courses.

Core Courses

INFO-I 501 Introduction to Informatics
INFO-H 611 Mathematical and Logical Foundations of Informatics
LIS-S 511 Database Design
PBHL-B 670 Introduction to Biostatistics in R (or INFO-I 590 Introduction to Statistics in R, or PSY 60000 Statistical Inference, or INFO-B 518 Applied Statistical Methods for Biomedical Informatics)
INFO-H 515 Statistical Learning (prerequisite: PBHL-B 670 or PSY 60000)
INFO-H 516 Cloud Computing for Data Science (prerequisites: graduate database course and programming experience)

Students may test out of INFO-H 611 and LIS-S 511. Students do not receive credit toward their required 30 credit hours by testing out of a course. However, they may instead replace the course with a specialization course or approved elective.

Specialization Courses

Students may replace specialization courses with approved electives with the permission of the program director.

Thesis or Project

The Thesis/Project is available to highly motivated students ready to carry out publishable research. Students must prepare a prospectus and gain a commitment from a primary faculty advisor with research interests in data science by the end of the first semester. By the end of the second semester, students must complete a course on research design and methods (e.g., INFO-I 575, LIS-S 506, or STAT 514).

The thesis or project must be completed in two semesters or in a semester and summer. Thesis students register for a total of 6 credits and project students register for a total of 3–6 credits of INFO-H 695 Thesis/Project in Applied Data Science. Students are required to prepare and defend a research proposal with a timeline of deliverables in addition to the thesis or project.

The following approved electives are available in the fall and spring semesters.

CIT 51600 Database Security (3 cr.)
CIT 52800 Information Security Risk Assessment (3 cr.)
CIT 53200 Wireless Security & Technology (3 cr.)
CIT 55510 Network Security (3 cr.)
CIT 56200 Mobile and Network Forensics (3 cr.)
CSCI 52000 Computational Methods in Analysis (3 cr.)
CSCI 54900 Intelligent Systems (3 cr.)
CSCI 55700 Image Processing and Computer Vision (3 cr.)
CSCI 58000 Algorithm Design, Analysis, and Implementation (3 cr.)
ECON-E 570 Fundamentals of Statistics and Econometrics (3 cr.)
ECON-E 574 Applied Econometrics and Forecasting (3 cr.)
GEOG-G 535 Environmental Remote Sensing (3 cr.)
HPER-T 591 Introduction to Statistics in Public Health (3 cr.)
INFO-B 519 Introduction to Bioinformatics (3 cr.)
INFO-B 530 Foundations of Health Informatics (3 cr.)
INFO-B 573 Programming for Science Informatics (3 cr.)
INFO-B 578 Data Analysis for Clinical and Administrative Decision-Making (3 cr.)
INFO-H 541 Interaction Design Practice (3 cr.)
INFO-H 559 Media and Technology Entrepreneurship (3 cr.)
INFO-I 698 Research in Informatics (1-3 cr., also available in Summer)
INFO-I 590 Information Governance (3 cr.)
INFO-I 595 Professional Internship (1-3 cr., also available in Summer)
LIS-S 503 Organization and Representation of Knowledge and Information (3 cr.)
LIS-S 541 Information Policy (3 cr.)
PBHL-B 571 Biostatistics Method I: Linear Models in Public Health (4 cr.)
PBHL-B 572 Biostatistics Method II: Categorical Data Analysis (4 cr.)
PBHL-B 574 Biostatistics Methods IV: Applied Longitudinal Data Analysis (3 cr.)
PBHL-B 582 Introduction to Clinical Trials (3 cr.)
PBHL-B 583 Applied Multivariate Analysis for Public Health (3 cr.)
PBHL-B 585 Analysis of Observational Studies (3 cr.)
PHST-P 530 Data for Good (Topics in Philanthropic Studies) (3 cr.)
SPEA-J 520 Mapping and Analysis for Public Safety (3 cr.)
SPEA-J 524 Emergency Management (3 cr.)
STAT 51100 Statistical Methods 1 (3 cr.)
STAT 51200 Applied Regression Analysis (3 cr.)
STAT 51600 Basic Probability Applications (3 cr.)
STAT 51900 Introduction to Probability (3 cr.)
STAT 52100 Statistical Computing (3 cr.)
STAT 52200 Sampling and Survey Techniques (3 cr.)
STAT 52400 Applied Multivariate Analysis (3 cr.)
STAT 52500 Generalized Linear Model (3 cr.)
STAT 52800 Mathematical Statistics I (3 cr.)
STAT 52900 Applied Decision Theory and Bayesian Statistics (3 cr.)
STAT 53600 Introduction to Survival Analysis (3 cr.)
STAT 61900 Probability Theory (3 cr.)
STAT 62800 Advanced Statistical Inference (3 cr.)

Substitute courses accommodate special interests and scheduling needs, such as for an online course. A substitute course satisfies the core or specialization course requirement.

A student wishing to substitute a course or take an elective course from our Data Science program in Bloomington must apply for graduate non-degree status at least one month before the start of the semester.

Students may be able to transfer in approved graduate courses from an accredited institution.

International students on an F-1 visa may only take one online course per semester. They must typically take 8 credit hours per semester except in their final semester.. These limitations apply to fall and spring semesters but not summer sessions.

LIS-S 511, INFO-B 512, or INFO-B 556
- CSCI 54100 Database Systems (3 cr., prerequisite: CSCI 44300 Database Systems or equivalent)
INFO-H 517
- CSCI 55200 Data Visualization
INFO-H 515
- CSCI 57300 Data Mining
- CSCI 57800 Statistical Machine Learning
- INFO-B 518 Applied Statistical Methods for Biomedical Informatics
- INFO-I 526 Applied Machine Learning (IUB)
INFO-H 516
- CSCI 59000 Cloud Computing
NEWM-N 510
- LIS-S 517 Web Programming
PBHL-B 670
- ECON-E 570 Fundamentals of Statistics and Econometrics
- HPER-T 591 Introduction to Statistics in Public Health
- INFO-B 518 Applied Statistical Methods for Biomedical Informatics
- PBHL-B 561 Introduction to Biostatistics I
- PSY 60000 Statistical Inference
- STAT 51100 Statistical Methods I
INFO-I 575
- LIS-S 506 Introduction to Research
- STAT 514 Design of Experiments
- TECH 50700 Measurement and Evaluation in Industry and Technology

Degree Maps

Fall Year 1

INFO-H 611 Mathematical and Logical Foundations of Informatics
INFO-I 501 Introduction to Informatics
LIS-S 511 Database Design
INFO-I 590 Introduction to Statistics in R, or PBHL-B 670 Introduction to Biostatistics in R

Spring Year 1

INFO-H 515 Statistical Learning
INFO-H 516 Cloud Computing for Data Science
NEWM-N 510 Web Database Development or another specialization course

Summer

Select a specialization course:

These summer courses are available online.

Fall Year 2

INFO-H 517 Visualization Design, Analysis, and Evaluation or another specialization course
INFO-H 518 Deep Learning Neural Networks or another specialization course
INFO-I 575 Informatics Research Design or another specialization course