We are looking forward to sharing many exciting stories and examples of analytics with all of you using a python programming language. This course includes examples of analytics in a wide variety of industries, and we hope that students will learn how you can use analytics in their careers and life. One of the most important aspects of this course is that you, the student, are getting hands-on experience creating analytics models; we, the course team, urge you to participate in the discussion forums and to use all the tools available to you while you are in the course!
INTENDED AUDIENCE : Management, Industrial Engineering and Computer Science Engineering Students
PREREQUISITES : Nill
INDUSTRY SUPPORT : Any analytics company
Course layout
Week 1 : Introduction to data analytics and Python fundamentals
Week 2 : Introduction to probability
Week 3 : Sampling and sampling distributions
Week 4 : Hypothesis testing
Week 5 : Two sample testing and introduction to ANOVA
Week 6 : Two way ANOVA and linear regression
Week 7 : Linear regression and multiple regression
Week 8 : Concepts of MLE and Logistic regression
Week 9 : ROC and Regression Analysis Model Building
Week 10 : c2 Test and introduction to cluster analysis
Week 11 : Clustering analysis
Week 12 : Classification and Regression Trees (CART)
Books and references
1. McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.".
2. Swaroop, C. H. (2003). A Byte of Python. Python Tutorial.
3. Ken Black, sixth Editing. Business Statistics for Contemporary Decision Making. “John Wiley & Sons, Inc”.
4. Anderson Sweeney Williams (2011). Statistics for Business and Economics. “Cengage Learning”.
5. Douglas C. Montgomery, George C. Runger (2002). Applied Statistics & Probability for Engineering. “John Wiley & Sons, Inc”
6. Jay L. Devore (2011). Probability and Statistics for Engineering and the Sciences. “Cengage Learning”.
7. David W. Hosmer, Stanley Lemeshow (2000). Applied logistic regression (Wiley Series in probability and statistics). “Wiley-Interscience Publication”.
8. Jiawei Han and Micheline Kamber (2006). Data Mining: Concepts and Techniques. “
9. Leonard Kaufman, Peter J. Rousseeuw (1990). Finding Groups in Data: An Introduction to Cluster Analysis. “John Wiley & Sons, Inc”.
