This is the first course in Data Analytics for undergraduate IS students. The objective of the course is to give students a broad overview of the various aspects of data analytics such as exploring, scrubbing, modeling, and interpreting data. Towards this, the course employs R as the primary tool with some use of Weka, Excel and Matlab. This being a first course, there is no rigorous development of data mining schemes, statistics, or theoretical treatment of aspects such as visualization, but the students are expected to do programming in R and develop a clear understanding of basic data modeling and ideas related to statistics.

Prerequisites (concepts):

None. An interest in analytical thinking and in data and a first course in Statistics will be helpful.

Contact Information:

Prashant Krishnamurthy
Office: DIST 718
Phone: 412-624-5144
E-mail: prashant AT mail DOT sis DOT pitt DOT edu
Course webpage: http://www2.sis.pitt.edu/~prashant/inf1092
Office hours: Wednesdays: 10:00 a.m. - 11.00 a.m. or by appointment
GSA: Xin Wang

Textbooks:

Wolfgang Jank, Business Analytics for Managers, Springer 2011
Lectures will be drawn from several sources (textbook, other books, articles, papers, and videos)

Required Software: R on Windows, Mac OS X, or Linux

References:

Data mining and Statistics

David J. Hand, Statistics, Sterling Press, 2008

G. Shmueli, N. R. Patel, and P.C. Bruce, Data Mining for Business Intelligence, John Wiley and Sons, 2010

J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2012

I.H. Witten, E. Frank, M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011

R and Related Material

N. Matloff, The Art of R Programming, No Starch Press, 2011

A. F. Zuur, E. N. Ieno, E.H.W.G. Meesters, A Beginner's Guide to R, Springer, 2009

J. Albert and M. Rizzo, R by Example, Springer, 2012

R. M. Heiberger and E. Neuwirth, R through Excel, Springer, 2009

Visualization

Stephen Few, Now You See it: Simple Visualization Techniques for Quantitative Analysis, Analytics Press, 2009

D. Cook and D. F. Swayne, Interactive and Dynamic Graphics for Data Analysis with R and GGobi, Springer, 2007

Websites and Webinars

Revolutionary Analytics

Dataists

Kaggle Tutorials

Grading:

Homework/In-Class Assignments 25%
Midterm 20%
Final 30%
Project 25%

Policies:

All work must be the student's own unless collaboration is explicitly permitted

Late assignments will not be accepted unless there are exceptional circumstances.

Homework is due ONE week after it is assigned unless otherwise mentioned.

Homework will be assigned every week unless otherwise mentioned.

Check for homework on the webpage even if it is not explicitly mentioned in class

Labs (where assigned) will be due TWO weeks after assignment

Students are responsible for doing the labs and submitting the reports to me

Check for lab instructions and changes on the webpage regularly

Keep checking the webpage for other changes regularly

All written work must be legible and clear to receive credit. Vagueness in your work leading to misinterpretation is not a valid reason for credit.

Course Outline:

This schedule is only a guideline and is subject to change depending on the progression of the course.

Week 1: Introduction; Big Picture

Weeks 2-3: Start with basic tools, especially R, Types of Data, Data Representation, Some public data sets

Week 4: Data Exploration I

Week 5: Data Exploration II

Weeks 6: Data Collection and Storage

Week 7: Review and Midterm exam;

Week 8: Data Exploration III

Week 9: Models 1: Regression

Week 10: Models 2: Making Models Flexible

Week 11: Data Scrubbing

Week 12: Models III: Dangers of To Much Data

Week 13: Fine Tuning Models

Week 14: Final Exam