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Social Data Analytics Minor

The Faculty of Arts and Social Sciences, with the Departments of Economics, Linguistics, Philosophy, and Political Science, along with the Departments of English, Gender, Sexuality and Women's Studies, and Statistics and Actuarial Science, and the School of Communication, offers a minor in Social Data Analytics (SDA). This program offers an interdisciplinary study of statistical and computational methods with an emphasis on the privacy, ethical, and societal issues surrounding technology and big data. It is intended to complement and build on the knowledge and deeper understanding of issues that students acquire from their major program of study in the Arts and Social Sciences and related disciplines. 

The program is managed by the Faculty of Arts and Social Sciences. An advisory committee consisting of representatives from the above departments serve as a liaison between participating departments and the program director.

Admission Requirements

Program admission is limited. Entry is via a formal minor program application and applications will be considered for both students entering Simon Fraser University and those already enrolled. Students may apply for admission to the minor program at any time. It is strongly recommended that students contact the Social Data Analytics advisor or program director early about admission and scheduling.

Program Requirements

Students must complete at least 27 units as follows:

Complete all of:

SDA 250 - Computational Text Analysis (4)

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.

Section Instructor Day/Time Location
D100 We 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
SDA 270 - Data, Ethics and Society (3)

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.

Section Instructor Day/Time Location
D100 Chelsea Rosenthal
Mo 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
SDA 490 - Capstone Project Seminar (5)

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.

Complete one of:

ECON 233 - Introduction to Economics Data and Statistics (4)

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; 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.

Section Instructor Day/Time Location
D100 Brian Krauth
Tu 10:30 AM – 12:20 PM
Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
D101 Tu 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Tu 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D103 We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D106 Th 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
POL 201 - Introductory Quantitative Methods in Political Science (4)

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.

Section Instructor Day/Time Location
D100 Mark Pickup
Mo 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Mo 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 2:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D103 Mo 4:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
STAT 203 - Introduction to Statistics for the Social Sciences (3)

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 200, 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.

Section Instructor Day/Time Location
D100 Scott Pai
Mo 10:30 AM – 12:20 PM
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD

Elective Requirements

Students complete a total of at least 12 elective units, as follows:

No more than four units of which may come from:

CMNS 333 - Digital Policies in a Global Context: Current Issues, Concepts and Analysis (4)

We will investigate current policy issues and case studies from Canada and around the world to examine communication policy as a field shaped by change and continuity. Students will get an introduction to communication policy analysis with the aim to sneak and write analytically about current challenges such as network neutrality, copyright, content regulation and data protection. Students will discuss the tensions that arise between the national-regional and global jurisdictions, and how these are relevant to their everyday life. Prerequisite: One of CMNS 230 or 240; and one of CMNS 202 (or 262) or 261.

CMNS 353 - Topics in Technology and Society (4)

Examination of the emergence and shaping of information and communication technologies 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. May repeat for credit if topic studied is different. Prerequisite: CMNS 253W and one of CMNS 201W (201 or 260) or CMNS 202 (or 262). Recommended: CMNS 362.

Section Instructor Day/Time Location
D100 Sun Ha Hong
Th 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D101 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Th 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D103 Th 5:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
D104 Th 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
D105 Tu 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D106 Tu 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
ENGL 363 - Studies in Digital Humanities: Theory and Practice (4)

Focuses on the study and application of theories, methods, and tools in the field of digital humanities. Addresses questions of how digital literature and the digitization of texts, digital communication technologies, and computational methods reshape our understanding of literature and other media. Prerequisite: Two 100 division English courses, and two 200 division English courses.

GSWS 399 - Gender, Sex and Numbers (4)

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.

POL 318 - Fake News and Alt-Facts: Navigating Post-Truths Politics (4)

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.

POL 426W - Political Behavior (4)

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.

No more than four units of which may come from:

ECON 334 - Data Visualization and Economic Analysis (3)

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, ECON 103 or ECON 105. 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.

POL 390 - Data Visualization and Political Analysis (3)

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.

Section Instructor Day/Time Location
D100 Edana Beauvais
We 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
STAT 240 - Introduction to Data Science (3)

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, or BUEC 232, and one of CMPT 102, CMPT 120, CMPT 125, CMPT 128, CMPT 129, CMPT 130, or permission of the instructor. STAT 260 is also recommended. Quantitative.

Section Instructor Day/Time Location
D100 Lloyd Elliott
Mo 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D101 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D103 Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D104 Mo 5:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
D105 Mo 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
D106 Mo 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
D107 Mo 8:30 PM – 9:20 PM
REMOTE LEARNING, Burnaby
STAT 310 - Introduction to Data Science for the Social Sciences (2)

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 Science and one of STAT 201, STAT 203, STAT 205, STAT 270, BUEC 232, or POL 201. 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.

and STAT 311 - Data Science Laboratory for the Social Sciences (2)

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 Science and one of STAT 201, STAT 203, STAT 205, STAT 270, BUEC 232, or POL 201. 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.

Any of:

ECON 329 - Experimental Economics (3)

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 and ECON 105. 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.

ECON 335 - Introduction to Casual Inference and Policy Evaluation (3)

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.

Section Instructor Day/Time Location
D100 Simon Woodcock
Fr 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D101 Tu 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D106 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
ECON 435 - Econometric Methods (5)

The application of econometric techniques to the empirical investigation of economic issues. Prerequisite: ECON 201 or 301 and ECON (or BUEC) 333. Entry into this course requires a minimum CGPA of 3.0 or permission of the department. Quantitative.

GEOG 255 - Geographical Information Science I (3)

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. Prerequisite: GEOG 100 or 111 or permission of instructor. Students with credit for GEOG 354 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Yuhao Lu
Yuhao Lu
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 Th 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D102 Th 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D103 Fr 2:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
GEOG 355 - Geographical Information Science II (4)

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.

Section Instructor Day/Time Location
D100 Nadine Schuurman
Mo 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 Mo 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D102 Tu 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D103 Tu 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
PHIL 315 - Formal Methods (3)

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 or STAT 270. Students with credit for COGS 315 cannot take this course for further credit.

Section Instructor Day/Time Location
D100 Jennifer Wang
Mo 12:30 PM – 1:20 PM
Th 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
POL 315 - Intermediate Quantitative Methods (4)

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.

Section Instructor Day/Time Location
D100 Edana Beauvais
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
POL 488 - Topics in Empirical Research Design and Analysis (4)

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.

STAT 302 - Analysis of Experimental and Observational Data (3)

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 Statistics major or honours program. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232. Quantitative.

Section Instructor Day/Time Location
D100 Brad McNeney
Mo 12:30 PM – 2:20 PM
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD
STAT 360 - Advanced R for Data Science (2)

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. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 361.

Section Instructor Day/Time Location
D100 Brad McNeney
Tu 2:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
and STAT 361 - Laboratory for Advanced R for Data Science (1)

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. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 360.

Section Instructor Day/Time Location
D100 Brad McNeney
Th 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D200 Brad McNeney
Th 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D300 Brad McNeney
Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D400 Brad McNeney
Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
STAT 452 - Statistical Learning and Prediction (3)

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 BUEC 333 or equivalent. Quantitative.

With approval of the program director, up to six units of relevant courses from other departments may be counted for the minor.

Faculty of Arts and Social Sciences Degree Requirements

For all bachelor of arts (BA) programs, students complete 120 units, which includes

  • at least 60 units that must be completed at Simon Fraser University
  • at least 45 upper division units, of which at least 30 upper division units must be completed at Simon Fraser University
  • at least 65 units (including 21 upper division units) in Faculty of Arts and Social Sciences courses
  • satisfaction of the writing, quantitative, and breadth requirements
  • an overall cumulative grade point average (CGPA) and upper division CGPA of at least 2.0, and minimum CGPA and upper division CGPA of at least 2.0 across all units attempted in each subject that is a major, a joint major, a minor, or an extended minor. FASS Departments may define specific requirements for their respective programs.

For students in other Faculties, please check your Faculty's overall degree requirements: https://www.sfu.ca/students/calendar/faculties-research.html

Writing, Quantitative, and Breadth Requirements

Students admitted to Simon Fraser University beginning in the fall 2006 term must meet writing, quantitative and breadth requirements as part of any degree program they may undertake. See Writing, Quantitative, and Breadth Requirements for university-wide information.

WQB Graduation Requirements

A grade of C- or better is required to earn W, Q or B credit

Requirement

Units

Notes
W - Writing

6

Must include at least one upper division course, taken at Simon Fraser University within the student’s major subject
Q - Quantitative

6

Q courses may be lower or upper division
B - Breadth

18

Designated Breadth Must be outside the student’s major subject, and may be lower or upper division
6 units Social Sciences: B-Soc
6 units Humanities: B-Hum
6 units Sciences: B-Sci

6

Additional Breadth 6 units outside the student’s major subject (may or may not be B-designated courses, and will likely help fulfil individual degree program requirements)

Students choosing to complete a joint major, joint honours, double major, two extended minors, an extended minor and a minor, or two minors may satisfy the breadth requirements (designated or not designated) with courses completed in either one or both program areas.