COURSE DESCRIPTION 
This course is an introduction to statistical thinking and concepts, beginning with basic probability theory. The course concludes with selected statistical methods useful for data exploration and description of vectorvalued data, a common setup in modern data analysis applications. Python and/or R will be used for practical implementation of all numerical and graphical procedures, including simulations. Prerequisites Common requirements for the Semester in Mathematical Tools for Data Science. 
COURSE GOALS 
On completion of the course, students will:

COURSE CONTENTS 
1. Introduction (0.5 week) 2. Probability Theory (2 weeks) 3. Random Variables (2.5 weeks) Mean and variance. Discrete families: Bernoulli, binomial, geometric and Poisson densities. Continuous families: exponential and normal densities. Multivariate normal distribution. 4. Graphical methods for exploring univariate and multivariate data (1.5 weeks) 5. Statistical Inference (4.5 weeks) 6. Regression Models (3 weeks) Bibliography
Support Sessions 2 hours a week with a teaching assistant Grading Two midterm exams (25% each), homework (20%) and a final project (30%) 