Digging the Digital Era: A Data Science Primer|
|This course may be repeated for credit.|
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. We describe how "big data" analysis affects both business practices and public policy and discuss applications in different areas/disciplines. We also discuss the ethical, legal, and privacy dimensions of big data analysis. 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. Students will also engage in a semester-long project where they will access and use data from social media (Twitter) to address their own research questions.
||Gen Ed Area Dept:
NSM QAC, SBS QAC|
|Course Format: Lecture / Discussion||Grading Mode: Graded|
||Fulfills a Major Requirement for: (DATA-MN)
||Past Enrollment Probability: 50% - 74%
|Special Attributes: CQC|
Jeffrey M. Stanton, INTRODUCTION TO DATA SCIENCE
Several Journal and newspaper/magazine articles e.g.
Wolfram, S. (April 24, 2013). Data Science of the Facebook World.
Preis, T., H. S. Moat, et al. (2013). "Quantifying Trading Behavior in Financial Markets Using Google Trends." Sci. Rep. 3.
United Nations Global Pulse. (December 8, 2011). Unemployment Through the Lens of Social Media.
Lohr S. (March 23, 2013). Big Data Is opening Doors, but Maybe Too Many. Retrieved from
Collins, H. (May 24, 2013). Predicting Crime Using Analytics and Big Data. Retrieved from
Sadilek, A., H. Kautz and V Silenzio. (May 20. 2012). Modeling Spread of Disease from Social Interactions.
|Examination and Assignments: |
1 take home exam
1 project (oral presentation)
Multiple independent assignments
|Instructor(s): Kaparakis,Emmanuel I. Oleinikov,Pavel V Times: ..T.R.. 01:10PM-02:30PM; Location: ALLB204; |
|Total Enrollment Limit: 30||SR major: 0||JR major: 0|| || |
|Seats Available: -8||GRAD: 2||SR non-major: 7||JR non-major: 7||SO: 7||FR: 7|