Data Science
Instructor:
Jagannath Aghav
Text Books:
- Cathy O'Neil and Rachel Schutt, "Doing Data Science, Straight Talk From The Frontline," O'Reilly Publications, 408 pp, 2014
- Jure Leskovec, Anand Rajaraman, and Jeffrey D Ullman, "Mining of Massive Datasets," Cambridge University Press, 2014
- Ethem Alpaydin, “Introduction to Machine Learning,” 3rd ed, MIT Press, 640 pp, August 2014
Reference books:
- Avrim Blum, John Hopcroft and Ravindran Kannan, "Foundations of Data Science," (Note: this is a book currently being written by the three authors. The authors have made the first draft of their notes for the book available online. The material is intended for a modern theoretical course in computer science.)
- Thomas H. Davenport, Jeanne G. Harris and Robert Morison, “Analytics at Work: Smarter Decisions, Better Results”, Harvard Business Press, 2010
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Elements of Statistical Learning, Data Mining, Inference, Prediction, 2nd ed, Springer Verlag, 2009
Course Outline:
1. Data Science- Introduction
2. Statistical Inference
3. Exploratory Data Analysis and the Data Science Process
4. Naive Bayes Algorithm
5. Extracting Meaning From Data
6. Recommendation Systems
7. Social-Networks
8. Spam Filters
9. Data Visualization
10. Machine Learning Algorithms & Pipelining
11. Kaggle : Solutions and Process Participation
(Datasets, Big Data Challenges, Deep Learning, Python & R Packages, Regression)
Course Learning Outcomes:
Students will be able to:
- Compare data sets by understanding the importance of data science processes
- Analyze and implement the statistical descriptors on a chosen dataset
- Demonstrate case studies on social networks, recommender systems, and
- Investigate solutions to the state- of-the-art problems/competitions.
Additional Links:
1. http://www.kdnuggets.com/2015/06/ top-20-r-machine-learning-packages.html Machine learning and data science packages of R
2. http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ Visual introduction to the state of the art data science and machine learning