In MSSP, training in statistical theory, methods, and computing are core building blocks to your preparation as a data scientist. These tools will be learned through your coursework, and put to work immediately through the Practicum.


Our mantra is that theory informs principle, and principle informs practice.  Your training in Conceptual Foundations of Statistics (MA677) not only provides you with the necessary foundations in probability and statistical inference — it also shows you why they matter in practice.

Diagram of theory pointing to principle, principle to practice, practice pointing to theory, etc.



The toolbox of today’s practicing data scientist is filled with statistical and machine learning methods. Over just two semesters you receive exposure to a broad and rich collection of methods necessary for your success, in the courses Applied Statistical Modeling (MA678) and Applied Statistical Machine Learning (MA679). 


The ability to navigate the modern computing environment is critical for success as a data scientist, allowing you to combine data with theory and methods to create knowledge.  Between our Data Science with R (MA615) and our Statistics Practicum (MA675-6), you are exposed not only to multiple software environments (e.g., R, python, SAS, SQL) but also related elements like software versioning, visualization, and reproducible research.

Logos of computing software companies like Python, R studio, SQL, SAS