| 1st Year, Semester 1 (Fall) |
| Databases |
Build scalable data systems with SQL and NoSQL databases. Focus on data modeling and analytics. |
| Programming for Data Science |
Basic Python coding and algorithms, numpy, pandas, accessing APIs, and optimization with applications in Machine Learning. |
| Applied Statistics 1 |
Descriptive and inferential statistics, Linear regression, Probability theory, Resampling, R implementations. |
| Data Munging |
Methods for gathering, organizing, and reshaping structured and unstructured data for exploratory analysis in R, Python. |
| 1st Year, January |
| Industry Workshops |
Small workshops on current topics organized by industry partners (Lexis Nexis, SAS, NVIDIA) |
| 1st Year, Semester 2 (Spring) |
| Distributed Computing |
Build cloud data pipelines and ML systems: Infrastructure automation with Azure, distributed processing in Databricks. |
| Machine Learning |
Supervised and unsupervised learning using traditional machine learning methods, Python implementations. |
| Applied Statistics 2 |
Multiple Linear Regression Models, Model formulation, fitting, selection and evaluation. |
| Data Viz. & Communication |
Interactive graphs for visualizing relationships, Maps, Dashboards, Effective communication & presentation |
| 2nd Year, Semester 3 (Fall) |
| Deep Learning and AI |
Neural Networks, CNNs, RNNs, Transformers, and intro to Large Language Models, vectorDBs, and RAG workflows. |
| Statistical Modeling |
Matrix algebra, Generalized Linear/Additive Models, Time series, Bayesian inference & modeling, Survival analysis. |
| Practical Data Science |
Apply knowledge to real-world datasets and projects, interacting with external sponsors and experts. |
| 2nd Year, Semester 4 (Spring) |
| Industry Practicum |
Work as part of a data science team with one of our industry partners, or use our career center to find your own. |
