Foundations of Data Science

Learn how to interpret, understand and correctly apply fundamental terminology and techniques in future data analysis situations. Explore the theoretical aspects of probability and statistical inference, including basic probability, random variables, univariate and bivariate probability distributions, statistics, likelihood, point and interval estimation, hypothesis testing, and the frameworks underlying linear and logistic regression and Naive Bayes. Mathematical details are supplemented with computer-based examples and exercises (e.g. visualization and simulation).

Designed to teach you how to approach, analyze and interpret data, topics include data input/output, processing, exploratory analysis, clustering, common regression and classification models (including those of classical statistics and of machine learning), and experimental design. Practice using these methods on real-world data and subsequently apply them when analyzing data in the program's Data Science Capstone course.

Prerequisite: Probability & Statistics for Data Science

Explore the most common forms of graphical displays and their (mis)uses. Learn how to create well-designed graphs and understand them from a statistical perspective, while working with increasingly common, complex data structures (temporal, spatial, and text data). All assignments will be completed in R. Throughout the course, communication skills will play an important role.

Learn how to apply computational thinking to data processing and analysis problems through R programming language. Topics include defining and manipulating vectors, lists, and data frames; processing strings and applying regular expressions in string searches; input and output data; writing functions; applying numerical methods such as integration and optimization; working with date-and-time-based data; and applying unit testing.

In the capstone course, work with real-world data to apply the skills and knowledge acquired throughout the program. Supported by subject matter experts, you will have the opportunity to practice synthesizing and communicating results in a clear and concise manner.