Introduces basics of text analysis through Python programming. Core concepts include: data capture and manipulation; data cleaning and preprocessing; database management; big data; natural language processing; introductory machine learning; text classification. Students with credit for LING 250 may not take this course for further credit.

This course would introduce students to the ethical, legal, and privacy issues surrounding the collection and use of big data and the implications of these for vulnerable populations. Students with credit for PHIL 270 may not take this course for further credit.

Final capstone group project that applies the skills students have learned in their studies to gain experience tackling real-world policy and social problems and develop a portfolio that they can showcase to prospective employers or graduate programs. Topics vary. Prerequisite: Admission into the Social Data Analytics Minor and 18 units completed in the minor.

Introduces statistical methods, concepts and their application to economic data using both spreadsheets (e.g., Excel) and a specialized statistical programming language such as R. Prerequisite: MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 15 units. MATH 150, MATH 151, MATH 154, or MATH 157 may be taken concurrently with ECON 233. Students who have taken ECON 333 first may not then take this course for further credit. STAT 270 or BUS 232 will be accepted in lieu of this course.

Introduces quantitative research techniques in political science. Introduces important analytical and conceptual skills necessary to understand and evaluate quantitative political science research. Corequisite: POL 200W or permission of department. Quantitative.

Descriptive and inferential statistics aimed at students in the social sciences. Scales of measurement. Descriptive statistics. Measures of association. Hypothesis tests and confidence intervals. Students in Sociology and Anthropology are expected to take SA 255 before this course. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units including a research methods course such as SA 255, CRIM 220, POL 200W, or equivalent. Students cannot obtain credit for STAT 203 if they already have credit for - or are taking concurrently - STAT 101, 201, 205, 285, or any upper division STAT course. Quantitative.

Examination of the emergence and shaping of information and communication technologies and science in the digital age. Explores new media and social change between everyday life, social institutions, and various enterprises. Emphasis is placed on social context and relations of power. This course can be repeated once for credit if second topic is different (up to a maximum of two times). Prerequisite: 17 CMNS units with a minimum grade of C- or 45 units with a minimum CGPA of 2.00.

Study of the relation of literature and media (manuscript, print, visual, aural, digital, and/or oral) within their cultural and/or performative contexts. May be further organized by methodology (e.g. book history, textual scholarship, media studies, adaptation studies, digital humanities), historical period, or genre. This course may be repeated for credit if a different topic is taught. Prerequisite: 30 units or two 200-division English courses.

Through an examination of the social construction of numbers and other forms of quantitative data will provide an introduction to measurement and its use within social justice movements and policy circles. In analyzing such topics as the relationship between professional, state and community conceptualizations of quantitative evidence, students will make use of introductory statistical concepts, methods and argument. Prerequisite: 30 units. Quantitative.

Explores the emergence of post-truth politics; the rejection of expert and scientific opinion; and the emergence of "alternative facts" and "fake news" in political discourse on current issues such as climate change, immigration and the economy. Also explores corresponding increase in the ideological polarization in the US, Canada, and the UK and other European nations. Prerequisite: Six lower division units in Political Science or permission of the department. Students with credit for POL 339 Selected Topics in Comparative Government and Politics under the title Navigating the Post-Truth World may not take this course for further credit.

The study of political attitudes and behavior in Canada, the United States and other democratic states. Topics will include political culture, public opinion, elections and voting behavior. Prerequisite: POL 222, and eight upper division units in Political Science or the permission of the department. Writing/Quantitative.

Explores how to recognize and learn from patterns in data using modern statistical software for the purpose of economic analysis. Introduces students to techniques for managing, visualizing, and analyzing data to answer real-world economic questions. Prerequisite: ECON 233 or BUS 232 or STAT 270 or POL 201, with a minimum grade of C-; ECON 103 with a minimum grade of C-, or ECON 113 with a minimum grade of A-, or ECON 105 with a minimum grade of C-, or ECON 115 with a minimum grade of A-. Students with credit for POL 390, STAT 341, or STAT 452 may not take this course for further credit. Students with credit for ECON 387 under the title "Applied Data Analysis" may not take this course for further credit.

Social data and digital technologies are rapidly transforming politics and society, including election campaigns, how governments make policy, the targeting of consumers, and our interactions and connections with one another. This course offers a hands-on introduction to data science with an emphasis on data visualization for political and social analysis. Prerequisite: One of POL 201, ECON 233, STAT 203 or equivalent. Students with credit for ECON 334, ECON 387 under the title "Applied Data Analysis", or POL 339 under the title "Politics and Data Science" may not take this course for further credit. Quantitative.

Introduction to modern tools and methods for data acquisition, management, and visualization capable of scaling to Big Data. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, and one of CMPT 102, CMPT 120, CMPT 125, CMPT 128, CMPT 129, CMPT 130, all with a minimum grade of C- or permission of the instructor. STAT 260 is also recommended. Quantitative.

An introduction to modern tools and methods for data acquisition, management, visualization, and machine learning, capable of scaling to Big Data. No prior computer programming experience required. Examples will draw from the social sciences. This course may not be used to satisfy the upper division requirements of the statistics honours, major, or minor programs. Prerequisite: 60 units in subjects outside of the Faculties of Science and Applied Sciences and one of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a minimum grade of C-. Corequisite: STAT 311. Students who have taken STAT 240, STAT 440, or any 200-level or higher CMPT course first may not then take this course for further credit. Quantitative.

A hands-on application of modern tools and methods for data acquisition, management, visualization, and machine learning, capable of scaling to Big Data. No prior computer programming experience required. Projects will draw from the social sciences and integrate application area insight into the analytic toolkit from STAT 310. This course may not be used to satisfy the upper division requirements of the statistics honours, major, or minor programs. Prerequisite: 60 units in subjects outside of the Faculties of Science and Applied Sciences and one of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a minimum grade of C-. Corequisite: STAT 310. Students who have taken STAT 240, STAT 440, or any 200-level or higher CMPT course first may not then take this course for further credit. Quantitative.

Covers experimental methods that are used to test existing theories of rational and behavioural economic decision making in a number of environments related to markets, different institutions, as well as strategic situations. Introduces and discusses methodological tools needed to design, run and analyze experiments. Prerequisite: ECON 103 with a minimum grade of C- or ECON 113 with a minimum grade of A-; ECON 105 with a minimum grade of C- or ECON 115 with a minimum grade of A-. Students who have taken ECON 383 Selected Topics - Experimental Economics in Fall 2011, Fall 2012, Summer 2013, Spring 2014, Summer 2014, Fall 2018, or Spring 2019 may not take this course for further credit.

Provides an introduction to statistical methods used to analyze causal questions and evaluate policies. Discusses various approaches to drawing causal inferences from observational data. Corequisite: ECON 333. Students who have taken ECON 480 first may not then take this course for further credit.

The application of econometric techniques to the empirical investigation of economic issues. Prerequisite: ECON 201 and ECON (or BUEC) 333, all with a minimum grade of C-. Entry into this course requires a minimum CGPA of 3.0 or permission of the department. Quantitative.

A basic overview of Geographical Information Systems and Science; GIS software, hardware, data structures and models; spatial data, operations and algorithms; practical applications and limitations. Students with credit for GEOG 354 may not take this course for further credit. Quantitative.

An examination of technical components of GIS. Topics include spatial representations, generalization and data management; computational algebra and set theory; digital surfaces and terrain models. Prerequisite: GEOG 255. Quantitative.

Introduction to theoretical and applied issues in the computational processing of natural language. Prerequisite: LING 250 or SDA 250.

A survey of formal methods used in philosophy. Topics will include some of the following: propositional logic, predicate logic, formal syntax, formal semantics, the probability calculus, decision theory, game theory and formal causal modeling. Prerequisite: One of: PHIL 110, 210, 310, 314, MACM 101, BUEC 232, BUS 232, or STAT 270. Students with credit for COGS 315 cannot take this course for further credit.

Introduces intermediate quantitative methods and data analysis. Teaches students how to build statistical models and apply them to social and political research. Also covers the fundamentals of probability, sampling, and causal inference; students will learns how to conduct their own data-driven research. Prerequisite: POL 201 or permission of instructor. Quantitative.

Topics in statistical and computational methodologies that focus on research design, data collection, visualization, and analysis in political and social science. The topics vary and example topics include: time-series and longitudinal analysis, text analysis, network analysis, and computational social science. Students may repeat this course for further credit under a different topic. Prerequisite: POL 201, ECON 233, STAT 203, or equivalent.

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take the course for further credit. Quantitative.

Advanced R programming methods for data science. Tools for reproducible research. Version control. Data structures, subsetting, functions, environments, and debugging. Functional programming. Code performance: profiling, memory, integrating R and C++. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 361.

A hands-on application of advanced R programming methods for data science. Using the R concepts covered in STAT 360 and tools for reproducible research, students will work with different data structures, write functions, and debug and optimize the performance of their code. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 360.

An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.