Statistics for Social Sciences (STAT)

This introductory statistics course teaches students to organize, visualize, and summarize data, then apply probability concepts to address the enduring question: How can we use probability and statistics to tell if something is just random or actually meaningful? Students will learn to conduct hypothesis tests, interpret data visualizations, and evaluate statistical claims with appropriate skepticism, using technology tools to draw evidence-based conclusions for real-world decision-making. This course requires students to have basic proficiency through high school algebra.

Cross-disciplinary course on how and why scientists use statistics to describe and interpret information they gather. Topics include descriptive statistics and basic inferential statistics. *An additional course fee will be charged.

A Calculus-based introduction to probability and the application of mathematical principles to the collection, analysis, and presentation of data. Modern probability concepts, discrete/continuous models, multivariate distributions, sampling distributions and applications. This course prepares students for the preliminary P/1 exam of the Society of Actuaries and Casualty Actuarial Society. Students should be comfortable with Calculus notation and methods. No previous knowledge of probability or statistics is assumed.

The course focuses on estimation and statistical inference through modern parametric, nonparametric, and simulation/randomization methods. Further topics include maximum likelihood, hypothesis testing, confidence intervals, ANOVA, and linear regression.

Case study approach to topics in data science: importing, wrangling, visualizing, modeling, and communicating data, with an emphasis on big data. Statistical learning techniques for classification, tree-based methods, regression, and unsupervised learning.

Examine the ethical issues related to method validity and reproducibility, and social impact and responsibility. Topics include privacy and anonymity, data accuracy and validity, transparency, model misuse and misinterpretation, and social responsibility and harm.

Use of mathematical applications and markup languages with an emphasis on R, R-studio, and LateX. Topics include statistical computing, functional programming with data analysis, simulation techniques, pseudo-random number generators, sampling techniques such as bootstrapping, and Markov chain Monte Carlo methods.

Project based application of previous mathematical statistics and computer science courses related to the Data Science and Analytics program. Use of statistical software and programming languages to complete a report, or project relating to data analytics or data science.