Digging the Digital Era: A Data Science Primer
QAC 211
Spring 2022
| Section:
01
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| Course Cluster and Certificates: Applied Data Science Certificate |
The course introduces students to the practice of what has come to be known as data science. Using a multidisciplinary approach and data from a variety of sources that cover any aspect of everyday life--from credit card transactions to social media interactions and Web searches--data scientists try to analyze and predict events and behavior. The first part of the course defines the area and introduces basic concepts, tools, and emerging applications that will include a broad introduction to machine learning tools and algorithms. It will include a brief mathematical background and introduction to modeling across disciplines. In part two of the course, we work on data acquisition and management and introduce appropriate programming and data management tools. In part three, we concentrate on basic analytical and visualization techniques as we explore and understand the emerging patterns. Using a learning-by-doing approach in a computing laboratory, students will learn how to write computer programs in R to access, organize, and analyze data through a series of small projects designed to illustrate the application of the techniques we develop for a variety of data sets and situations. The class will include hot topics like big data, privacy, and ethical issues around data, to name a few. Students will also engage in a semester-long project where they will address their own research questions working with "messy data." |
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| Credit: 1 |
Gen Ed Area Dept:
NSM QAC, SBS QAC |
| Course Format: Laboratory Course | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: None |
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Fulfills a Requirement for: (CADS)(DATA-MN)(HRAD-MN)(PSYC) |
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Past Enrollment Probability: 50% - 74% |
| SECTION 01 | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
Textbook:
Pathak, Manas, Beginning Data Science with R, Springer, 2014, available online through Wesleyan library: http://link.springer.com/book/10.1007/978-3-319-12066-9
Lovelace, Robin and James Cheshire, Introduction to visualising spatial data in R, online: https://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf
Supplementary books:
Scott Page - The Model Thinker: What You Need to Know to Make Data Work for You
Pathak, Manas, Beginning Data Science with R, Springer, 2014, available online through Wesleyan library: http://link.springer.com/book/10.1007/978-3-319-12066-9
Lovelace, Robin and James Cheshire, Introduction to visualising spatial data in R, online: https://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf
Articles:
Spasojevic, Nemanja et al., When-to-post on Social Networks, ArXiv e-prints, http://adsabs.harvard.edu/abs/2015arXiv150602089S
Eichstaedt, Johannes et al., Psychological Language on Twitter Predicts County-Level Heart Disease Mortality, Psychological Science, Vol. 26(2), 2015, http://pss.sagepub.com/content/26/2/159.abstract
Nofer, Michael, The Value of Social Media for Predicting Stock Returns: Preconditions, Instruments and Performance Analysis, Springer, 2015,
Mayer-Schoenberger, Viktor and Kenneth Cukier, The Rise of Big Data: How It's Changing the Way We Think About the World, Foreign Affairs, Vol. 92(3), 2013
Tene, Omar and Jules Polonetsky, Big Data for All: Privacy and User Control in the Age of Analytics, Northwestern Journal of Technology and Intellectual Property, Vol. 11(5), April 2013
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Examinations and Assignments:
Several homework assignments, a course project (Presentation &report). Part of the grade depends on in-class participation/preparedness. |
Additional Requirements and/or Comments:
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| Instructor(s): Gooyabadi,Maryam Times: ..T.R.. 08:50AM-10:10AM; Location: ALLB204; |
| Total Enrollment Limit: 18 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 3 | GRAD: X | SR non-major: 2 | JR non-major: 4 | SO: 7 | FR: 5 |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 5 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 1 | Unranked: 4 |
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