Course Code: DA241M	Course Name: Mathematical Foundations of Data Science	Credits: 3-0-0-6
Pre-requisite: None
Syllabus: Fundamentals: Review of linear algebra and multi-variate calculus from data science perspective. Probability and random variables: Basics of Probability Theory, Conditional Probability, Bayes’ Theorem, Random Variables, Discrete and Continuous Distributions, Moments, Law of large numbers and Central Limit Theorem. Statistical inference: Parametric and nonparametric methods, Point estimation, Confidence Intervals, Maximum Likelihood Estimators; Hypothesis testing; Bayesian Inference. Optimization: Unconstrained and Constrained optimization for single and multiple variables: Gradient descent methods, Newton’s method, Simplex method; Convexity and duality.
Textbooks: M. H. DeGroot, and M. J. Schervish, Probability and statistics, 4th Ed., Pearson Education, 2010. E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 4th Ed., Wiley India Pvt. Ltd., 2017.
References: Wasserman, L., All of statistics: a concise course in statistical inference, 1st Ed., Springer, New York, 2004. Strang, Gilbert. Linear algebra and learning from data. Cambridge: Wellesley-Cambridge Press, 2019.