A hands-on introduction to data science. Exploring key concepts related to data science, including exploratory data analysis, information visualization, text mining, and machine learning. Quantitative Reasoning. (University Program Group II-B: Quantitative and Mathematical Sciences)

Data visualization through Tableau to uncover data features and relationships. Focus on connecting to data, building different types of charts, table calculations, statistical modelling. Prerequisite: One of: DAS 150QR, STA 282QR, STA 382QR, STA 392, BIO 500, PSY 211QR, SOC 200, HSC 544, GEO 512.

Introduce R programming language for data science. Topics include data wrangling, management, manipulation, visualization and exploration using different R packages. Prerequisite: One of: DAS 150QR, STA 282QR, 382QR, 392, BIO 500, PSY 211QR, SOC 200, HSC 544, GEO 512.

Introduction to SAS programming and basic descriptive data analysis using SAS Enterprise Guide. This is a one-credit, five-week module. Prerequisite: One of: DAS 150QR, STA 282QR, 382QR, 392, BIO 500, PSY 211QR, SOC 200, HSC 544, GEO 512.

Introduce Python programming language for data science. Topics include data wrangling, management, manipulation, visualization and exploration using Python. This is one-credit, five-week course. Identical to CPS 254. Credit may not be earned in more than one of these courses. Prerequisite: One of: BIO 500, DAS 150QR, ECO 285, GEO 512, HSC 544, PSY211QR, SOC 200QR, STA 282QR, STA 382QR, STA 392.

The course explores ethics related to data science. Topics include data ownership and validity, societal consequences; maintenance of accurate and consistent data over its life. Prerequisite: One of: DAS 150QR, STA 282QR, 382QR, 392, BIO 500, PSY 211QR, SOC 200, HSC 544, GEO 512.

Topics include data exploration and analysis. Various techniques will be utilized to explore and visualize real-world massive datasets from different domains. Prerequisite: DAS 252.

Techniques for data science. Topics include unsupervised techniques and basic predictive modeling techniques. Prerequisites: DAS 251, 252, 253, 350.

Predictive modeling techniques for data science. Topics include tree models, shrinkage techniques, support vector machine, neural network, deep learning and Naïve Bayes. Prerequisites: DAS 450.

Students work on independent research projects. Problems may be from internship experience, prior courses or another source, subject to instructor approval. Prerequisites: DAS 460; Senior Standing.