Statistics

In this course, students learn the skills to write Python code to implement statistical and machine learning algorithms that can be applied in a range of contexts. Each week the course covers an aspect of computer coding using examples and exercises that drawn on bioscience contexts. Topics will include: probability, maximum likelihood, Bayes theorem, supervised learning: regression and classification, unsupervised learning: dimensionality reduction and clustering, model evaluation and improvement, reinforcement learning, and neural networks and deep learning.

Does income inequality lead to political polarization? Is social class related to parenting? What accounts for income inequality between ethnic groups in the labor market? We will use statistics and programming to answer these questions throughout the semester. This course introduces tools to summarize the characteristics of data and offers methods to draw conclusions about population from samples. This course focuses on applying statistics, analyzing data, and interpreting results. Although very basic calculation skills are required (e.g., +, -, ×, ÷, √), you do not need further mathematic knowledge to be successful in this class. 

This course offers an introduction to probability theory for students with knowledge of elementary calculus. The course covers not only the mathematics of probability theory but works through diverse examples to illustrate the wide scope of applicability of probability, such as in engineering and computing, social, and management sciences. Topics covered include counting methods, sample space and events, axioms of probability, conditional probability, independence, random variables, discrete and continuous distributions, joint and marginal distributions, conditional distribution, independence of random variables, expectation, conditional expectation, moment generating function, central limit theorem, and weak law of large numbers.

The course provides an introduction to the ideas underlying the calculation of risk from a Bayesian and frequentist standpoint, and the structure of rational, consistent decision making. It is primarily intended for third and fourth year undergraduate students and taught postgraduate students registered on the degree programs offered by the Department of Statistical Science.