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Data Science Programming

This material has been developed for the course MSA8010 in the Master of Science in Analytics at the J. Mack Robinson College of Business at Georgia State University.

Topics and Schedule

The schedule below applies to all sections for Fall 2017. Please, refer to this page for any changes.

# Track I Track II Topic Reading
1 Mon, August 21 We, August 23 UNIX file system, command line tools, Jupyter notebook (online)
2 Mon, August 28 We, August 30 Python programming language, Review (online)
3 Mon, Sept 11 We, Sept 6 Numpy and linear algebra operations (online)
4 Mon, Sept 18 We, Sept 13 Pandas data frames, loading data, data-table manipulation (online)
5 Mon, Sept 25 We, Sept 20 Operating with multiple tables, joining and reshaping (online)
6 Mon, Oct 2 We, Sept 27 Descriptive statistics with Pandas data tables (online)
7 Mon, Oct 9 We, Oct 4 Data visualization (online)
8 Mon, Oct 16 We, Oct 11 Machine Learning I ML Chapter 2, 8
9 Mon, Oct 23 We, Oct 18 Machine Learning II ML Chapter 3
10 Mon, Oct 30 We, Oct 25 Information Based Learning ML Chapter 4
11 Mon, Nov 6 We, Nov 1 Similarity Based Learning ML Chapter 5
12 Mon, Nov 13 We, Nov 8 Probability Based Learning ML Chapter 6
13 Mon, Nov 27 We, Nov 15 Error Based Learning ML Chapter 7
14 Mon, Dec 4 We, Nov 29 Presentations
TBA TBA Final Exam

Text Book

Book Cover

Fundamentals of Machine Learning for Predictive Data Analytics

by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy MIT-Press

  • Hardcover: ISBN 9780262029445, 624 pp., July 2015
  • eBook: ISBN 9780262331722, 624 pp., July 2015

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