Statistics in Data Science

(3 Credit Hours) · Undergraduate

This module provides a basic introduction to the ideas of probability and how simple probability models can be applied in a number of contexts. The topics covered in the module are: Sources of data, sampling, experiments, random variation Exploring data - graphical and numerical summaries Basic notions of probability - sample spaces, events, combination of events, counting Conditional probability and independence, Bayes' Theorem Random variables and probability distributions Binomial and related probability distributions Poisson distribution for counts, events over time Expectation - mean and variance Bivariate distributions - marginal and conditional probabilities, correlation and independence Normal distribution - properties, use of tables, central limit theorem and approximations Confidence interval in one sample problems using classical and computational (i.e. bootstrap) approaches. Hypothesis testing - Introducing null and alternative hypotheses, type I and II errors and p-values. Simple linear regression model. Use of R programming language for data exploration and probability model calculations.