Curriculum
The curriculum of the program supports both a graduate’s need to move between a multitude of roles and the specific competencies associated with project management, data management, analysis, model selection, statistics, and many of the technical tools (R studio, SQL, etc.).
Course Breakdown
| 12 credits from four required courses (All core courses will be taught using the Python programming language.) |
| 3 credits from lab courses (1 credit each) |
| 9 credits from elective courses (one 3-credit course from each domain area; see below) |
| 3 credits of practicum or internship |
| 3 credits of independent study or a capstone project |
| 30 CREDITS TOTAL |
Required Courses
12 credits from four required courses (All core courses will be taught using the Python programming language.)
| Programming and Math Fundamentals for Data Science |
| Statistical Methods for Data Science |
| Data Management and Visualization |
| Applied Data Mining and Machine Learning |
Lab Courses
3 credits from lab courses (1 credit each)
| ETL (Extract Transform Load) Techniques |
| Cloud Fundamentals |
| Data Visualization |
| Machine Learning Libraries |
| Thematic Maps |
| Version Control |
| Command Line Tools for Data Science |
| Using Databases and SQL (Structured Query Language) |
| Media Processing |
Elective Courses
9 credits from elective courses with one 3-credit course from each domain area – Mathematics and Statistics, Computer Science, and Social Sciences/Humanities.
| Mathematics and Statistics: |
|---|
| Linear Algebra and Applications |
| Visualizing Mathematics by Computer |
| Statistical Models |
| Statistical Reasoning |
| Time Series and Forecasting |
| Probability and Stochastic |
| Computer Science: |
|---|
| Data Structures and Algorithmic Problem Solving in Python |
| Parallel, Distributed and Cloud Computing |
| Database Systems |
| Big Data Algorithms |
| Applied Probability |
| Data Mining |
| Social Sciences/Humanities: |
|---|
| Introduction to Data Science |
| Applied Data Mining |
| Research Methods in Criminal Justice |
| Data Analysis in Criminal Justice |
| Special Topics: Data-Motivated Storytelling |
| Quantitative Methods I |
| Quantitative Methods II |
| Data Management |
| GIS for the Public Sector |
| Quantitative Methods |
| Using Archival Data to Study Children |
| Distant Reading with Text Analysis |
| Creative Coding |
| Information security management (aligned with CISSP certification) |
| Managing Projects and IT |
Practicum or Internship
3 credits from either a practicum or internship.
Directed Independent Study or Capstone
3 credits from either a direct independent study or a capstone project.