Master of Data Science for Public Policy  

Mathematics for data science

This course aims to deliver a compact and tailored introduction to the core mathematical concepts of data science. Linear algebra, probability theory, statistics, and optimisation are mathematical pillars underlying the practice of data science. The course covers foundational mathematical concepts such as statistical estimation, norms, matrix algebra, Lagrange Multipliers and many more in theory and practice. Upon completing the course, students will have a broad knowledge of linear algebra, probability theory, statistics, and optimisation necessary to understand the theoretical underpinnings of modern statistics and machine learning methods.

This course is for 1st year MDS students only.


  • Slava Jankin , Professor of Data Science and Public Policy
  • Lynn Kaack , Assistant Professor of Computer Science and Public Policy