MIDS Courses

A total of 42 credits will be required to complete the MIDS degree.

Tuition includes 4 electives. Additional electives may be taken (in theory a student could take up to eight), but some electives may require additional tuition payments if they are taken after the initial 4 electives are fulfilled.

Graduate courses at the 500, 600 level are open to juniors, seniors and to sophomores who have declared their major. Undeclared sophomores and very rarely freshmen require special permission. Graduate courses at the 700 to 900 level are not open to undergraduates, although exceptions are made on rare occasions.

Data Science Dialogues

IDS 791
Every Semester
Credits: 1 credit each semester

A series of discussions that give students snapshots of data science projects from practitioners and researchers.

MIDS Workshops

IDS 898
Every Semester
Credits: 0.5 credit each semester

A series of workshops to gain soft skills such as interviewing, negotiating, and networking.

Data Science Ethics

IDS 704
2nd half of Spring 1
Credits: 1.5

Data science tools are not morally neutral. Almost all the tools we create in data science influence society in a meaningful way, and almost all the data models we implement make assumptions about society that may or may not represent reality.

Data Logic, Visualization, and Storytelling

IDS 707
1st half of Spring 1
Credits: 1.5

Principles of communicating the implications of a data analysis.

Students will cultivate the ability to think critically and skeptically about the questions they need to answer in a data project and the strategies they are using to answer them. Students will learn the principles behind effective data visualization and how to implement them in real analyses using Tableau software.

Data Management Systems

IDS 706
Spring 1
Credits: 3

Managing and retrieving data from common data storage modalities. 

Students will learn to implement, secure, utilize, and manage database systems. Students will learn about normalization theory and relational database systems; entity-relationship models and relational schemas; extract, transform, load functions and workflows; complex SQL queries; and non-relational databases including NoSQL databases.

Principles of Machine Learning

IDS 705
Spring 1
Credits: 3

Automating prediction and decision-making based on data and past experience. Students will learn how and when to apply supervised, unsupervised, and reinforcement learning techniques, and how to evaluate performance. Common pitfalls such as overfitting and data leakage will be explored and how they can be avoided.

Data Scraping and Introduction to Text Analysis

IDS 703
Fall 1
Credits: 3

Introduction to the rich opportunities for using textual data produced by websites, social media platforms, digitization of administrative and historical records, and new monitoring technologies to gain insights and make decisions. 

Modeling and Representation of Data

IDS 702
Fall 1
Credits: 3

Extract actionable insights and draw inference from real world datasets. Methods for dealing with outliers and missing data, data that does not conform to standard modeling assumptions, data representations and particularly time series data analysis.

Principles of causal inference and common frameworks for analysis. Develop critical thinking about issues that affect the success of models in data science. This course will lay the foundation for more in-depth study into statistical techniques for practical data analysis.

Data to Decision

IDS 701
Fall 1
Credits: 3

Introduction to using data to make practical decisions. Students will work in small groups to analyze and understand the implications of real, messy data sets. Teams will design and implement their own analysis plan in order to recommend a strategy for solving problems.

Practicing Data Science II

Spring 1

This course is focused on how to answer questions effectively using quantitative data. By the end of the course, students will be able to recognize different types of questions (e.g. descriptive, causal, and predictive questions), have an understanding of what methodological approaches are most appropriate for answering each type of question, be able to design and critically evaluate data analysis plans, and understand how to tailor their presentation of results to different audiences.

Practicing Data Science

Fall 1

In part one of this course, students will develop hands-on experience manipulating real-world data using a range of data science tools (including the command line, Python, Jupiter, Git, and GitHub). This part of the course will emphasize general coding skills in Python, as well as python packages that are typically used in data science. 

Example pre-approved electives that emphasize quantitative topics:

COMPSCI  516 - Database Systems (3 credits)
COMPSCI  532 - Design and Analysis of Algorithms (3 credits)
COMPSCI  550 - Advanced Computer Architecture (3 credits)
ECON  612 - Time Series Econometrics (3 credits)
ECON  613 - Applied Econometrics in Microeconomics (3 credits)
MATH  541 - Applied Stochastic Processes (3 credits)
MATH  561 - Numerical Linear Algebra, Optimization and Monte Carlo Simulation  (3 credits)
MATH  563 - Applied Computational Analysis (3 credits)
POLSCI  733 - Advanced Regression (3 credits)
POLSCI  748 - Advanced Quantitative Research Methods in Political Science (3 credits)
PSY  767 - Applied Correlation and Regression Analysis (3 credits)
PSY  768 - Applied Structural Equation Modeling (3 credits)
PSY  770 - Applied Multilevel Modeling (3 credits)
SOCIOL  720 - Survey Research Methods (3 credits)
SOCIOL  728 - Advanced Methods: Introduction to Social Networks (3 credits)
STA  444 - Statistical Modeling of Spatial and Time Series Data (1 credit)
STA  561D - Probabilistic Machine Learning (3 credits)
STA  601 - Bayesian and Modern Statistical Data Analysis (3 credits)
BIOSTAT  902 - Missing Data Analysis: Theory and Application (3 credits)
BIOSTAT  904 - Causal Inference (3 credits)

Example pre-approved electives that emphasize topics specific to a certain domain:

ARTHIST  508S - Art and Markets (3 credits)
CLST  544L - Introduction to Digital Archaeology (3 credits)
BME  574 - Modeling and Engineering Gene Circuits (3 credits)
CBB  540 - Statistical Methods for Computational Biology (3 credits)
ECON  620 - Game Theory with Applications of Economics and other Social Sciences (3 credits)
PJMS  361S - Algorithms, Journalism and the Public Interest (1 credit)
PSY  716 - Behavioral Decision Theory (3 units)
PSY  762 - Functional Magnetic Resonance Imaging (3 credits)
SOCIOL  534 - Topics in Population, Health, and Policy (3 credits)
SOCIOL  367S - Computational Social Science: Tools to Collect & Analyze Human Behavior Using Data from the Internet (1 credit)
NEUROBIO  735 - Quantitative Approaches in Neurobiology (3 credits)
PUBPOL  574 - Economic Evaluation of Sustainable Development (3 credits)
VMS  550S - Digital Humanities: Theory and Practice (3 credits)

Students will be able to major in specific concentrations that have been designed by the faculty to prepare students to specialize in popular topics.

Concentrations are composed of four to six electives and may have additional components to help students gain expertise in the concentration topic. Completing an established concentration will not require additional tuition, even if it requires more than 4 electives. However, if a student commits to finishing a specific concentration and wishes to take additional electives outside of the concentration, additional tuition may apply in some cases and will be determined on a case-by-case basis.

If you are considering choosing a concentration, please work with your advisor to tailor your elective choices.

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Biomedical Informatics
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Population Health
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Survey Methods
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Social Networks

Application Deadline for Fall 2020: Feb. 15th