This interdisciplinary undergraduate degree combines statistics, computer science, and domain-specific knowledge to extract insights and value from data. Students learn to collect, clean, analyze, and interpret large, complex datasets using advanced programming, machine learning, and statistical methods. The program focuses on turning data into actionable intelligence to support decision-making in business, science, and government.
Built to align with WHU’s mission, this program balances a data-rich technical foundation with a liberal arts context, enabling students to leverage innovative analytics to make a global impact.
Integrates foundational writing, ethics, and quantitative skills with technical data science capabilities
Elective choices allow students to focus on areas like AI, BI, Big Data, or ethics according to career interests.
The capstone enables students to apply what they’ve learned in real-world or research settings
Aligns with WHU’s vision of leveraging innovative technologies for global impact
Our comprehensive curriculum is structured into different blocks. Each block combines theoretical foundations and practical applications:
Providing essential skills across disciplines
| Code | Course title | Credits | Type |
|---|---|---|---|
| ENG 101 | Writing and Composition | 4 | Core |
| ENG 102 | Professional and Technical Writing | 4 | Core |
| MATH 110 / MATH 115 | Precalculus / Calculus for Business | 4 | Core |
| STAT 120 | Introductory Statistics | 4 | Core |
| HUM 210 | Critical Thinking | 4 | Core |
| PHIL 220 | Ethics in Technology | 4 | Core |
| ECON 10 | Macroeconomics | 4 | Core |
| ECON 102 | Microeconomics | 4 | Core |
| --- | Science or Arts elective | 4 | Elective |
| Credits required: 32 - 36 | |||
| Code | Course title | Credits | Type |
|---|---|---|---|
| DS 201 | Introduction to Data Science & Analytics | 4 | Core |
| DS 210 | Data Structures & Database Systems | 4 | Core |
| DS 220 | Applied Statistics & Probability | 4 | Core |
| DS 230 | Programming for Data Science (Python & R) | 4 | Core |
| DS 240 | Data Visualization & Storytelling | 4 | Core |
| Credits required: 20 | |||
Students choose five electives to tailor their pathway
| Code | Course title | Credits | Type |
|---|---|---|---|
| DS 301 | Machine Learning & Predictive Modeling | --- | Elective |
| DS 302 | Big Data Analytics (Hadoop, Spark) | --- | Elective |
| DS 303 | Natural Language Processing & Text Analytics | --- | Elective |
| DS 304 | Deep Learning & Neural Networks | --- | Elective |
| DS 305 | Business Intelligence & Dashboarding | --- | Elective |
| DS 306 | Data Ethics, Privacy & Governance | --- | Elective |
| DS 307 | Time Series & Forecasting Models | --- | Elective |
| DS 308 | Statistical Computing & Simulation | --- | Elective |
| Credits required: 20 | |||
Senior Capstone Project—choose one
| Code | Course title | Credits | Type |
|---|---|---|---|
| DS 400 | Directed Research Project (advisor-supervised) | 4 | Core |
| DS 400 | Industry Internship (practical experience in a data-driven organization) | 4 | Core |
| Component | Credits |
|---|---|
| Foundation & General education | 32 - 36 |
| Core Data Science courses | 20 |
| Advanced Data Science electives | 20 |
| Capstone experience | |
| Total credits: 128 | |
Graduates of this program have pursued various rewarding career paths:
Develops complex models and algorithms to solve business problems and predict trends
Interprets data to identify trends, create visualizations, and generate reports for business decisions
Develops, constructs, and maintains the architecture (e.g., databases, large-scale processing systems) for data generation and analysis
To be considered for admission, applicants must meet the following requirements: