Essentials of Data Science

Introduction to Data Science, representing data, data models, collecting data, storing data, processing data, describing data, types of data analytics, data visualization, massive data handling, data streaming, data sampling, and data warehousing.

Statistical foundations for Data Science, sampling and surveys, population, sampling methods, statistical learning, statistical modelling, algorithm modelling, descriptive statistics, graphical approaches, measures of central tendency, and measures of location of dispersions.

Probability distribution, inferential statistics, random variables, single sample tests, Bayesian inference, hypothesis generation and testing, two sample tests, Type I and Type II errors, confidence intervals, chi-square test, t-test, analysis of variance, correlation analysis, maximum likelihood test, ANOVA, and test of independence.

Introduction to machine learning, supervised learning, unsupervised learning, regression, ordinary least square regression, simple and multiple regression, regularization, singular value decomposition, PCA, random walks, Markov chains, causality, the Rubin causal model, potential outcomes, counterfactuals, causal inference from observational data, and data-driven decisions.