The Master of Science in Data Science (MS-DS) is a multidisciplinary graduate program that equips students with the skills to extract insights and knowledge from complex, large-scale data. It sits at the intersection of statistics, computer science, and domain-specific knowledge. The curriculum focuses on advanced statistical modeling, machine learning, data wrangling, data visualization, and database management. Students learn to use programming tools and cloud platforms to manage the entire data lifecycle, from acquisition and cleaning to analysis and communication of findings, enabling data-driven decision-making in organizations.
The program integrates strong mathematical, computational, and ethical foundations with advanced training in machine learning, big data, and applied analytics. It prepares graduates to develop, evaluate, and deploy data-driven solutions across diverse industries such as business, healthcare, technology, and public policy.
Probability, machine learning, big data, and applied practice
Emerging areas such as NLP, causal inference, healthcare analytics, reinforcement learning
Capstone + Industry Track ensures practical, hands-on skills
Our comprehensive curriculum is structured into different blocks. Each block combines theoretical foundations and practical applications:
| Code | Course title | Credits | Type |
|---|---|---|---|
| DS 501 | Introduction to Data Science | 3 | Core |
| DS 502 | Probability & Statistics for Data Science | 3 | Core |
| DS 503 | Machine Learning | 3 | Core |
| IE 503 | Business Models, Strategy & Competitive Advantage | 3 | Core |
| DS 504 | Big Data Systems & Processing | 3 | Core |
| DS 505 | Data Science Capstone Project & Presentation | 3 | Core |
| Credits required: 15 | |||
(Choose 1: 3 credits)
| Code | Course title | Credits | Type |
|---|---|---|---|
| DS 521 | Deep Learning & Neural Networks | 3 | Elective |
| DS 522 | Natural Language Processing | 3 | Elective |
| DS 523 | Optimization & Computational Linear Algebra | 3 | Elective |
| DS 524 | Responsible Data Science & Ethics | 3 | Elective |
| DS 525 | Time Series & Probabilistic Modeling | 3 | Elective |
| Credits required: 3 | |||
| Code | Course title | Credits | Type |
|---|---|---|---|
| DS 531 | Advanced Python for Data Science | 3 | Elective |
| DS 532 | Applied Machine Learning | 3 | Elective |
| DS 533 | Data Visualization & Information Design | 3 | Elective |
| DS 534 | Causal Inference in Machine Learning | 3 | Elective |
| DS 535 | Reinforcement Learning | 3 | Elective |
| DS 536 | Computational Social Science | 3 | Elective |
| DS 537 | Applied Data Science in finance | 3 | Elective |
| DS 538 | Applied Data Science in Healthcare | 3 | Elective |
| Credits required: 18 | |||
| Component | Credits |
|---|---|
| Required core courses | 15 |
| Focus electives | 3 |
| General electives | 18 |
| Total credits: 36 | |
Graduates of this program have pursued various rewarding career paths:
Analyze complex data to build models, create algorithms, and develop predictive systems to guide business strategy
Interpret data and turn it into actionable insights through reporting, visualization, and dashboarding to help organizations make informed decisions
Specialize in creating and managing BI and analytics solutions that facilitate data-driven decision-making within a business
To be considered for admission, applicants must meet the following requirements: