Master’s In Data Science Curriculum

Build scalable data systems with SQL and NoSQL databases. Focus on data modeling and analytics.

Basic Python coding and algorithms, numpy, pandas, accessing APIs, and optimization with applications in Machine Learning.

Descriptive and inferential statistics, Linear regression, Probability theory, Resampling, R implementations.

Methods for gathering, organizing, and reshaping structured and unstructured data for exploratory analysis in R, Python.

Small workshops on current topics organized by industry partners (Lexis Nexis, SAS, NVIDIA)

Build cloud data pipelines and ML systems: Infrastructure automation with Azure, distributed processing in Databricks.

Supervised and unsupervised learning using traditional machine learning methods, Python implementations.

Multiple Linear Regression Models, Model formulation, fitting, selection and evaluation.

Interactive graphs for visualizing relationships, Maps, Dashboards, Effective communication & presentation

Neural Networks, CNNs, RNNs, Transformers, and intro to Large Language Models, vectorDBs, and RAG workflows.

Matrix algebra, Generalized Linear/Additive Models, Time series, Bayesian inference & modeling, Survival analysis.

Apply knowledge to real-world datasets and projects, interacting with external sponsors and experts.

Work as part of a data science team with one of our industry partners, or use our career center to find your own.