Department of Computer Science & Engineering Data Analytics (CS40003) Dr. Debasis Samanta Associate Professor Department of Computer Science & Engineering

Today’s discussion… Semester organization Syllabus Course objective Course plan Reference and study materials Course web page Contact details

…Course organization Title: Data Analytics Code: CS40003 Credit: 3-0-0 = 3 Slot: G Timing Wednesday: 11:00-11:55 Thursday: 12:00-12:55 Friday: 08:00-08:55 Venue: NR321, Nalanda Complex, 3rd Floor

…Course objective This course will cover fundamental algorithms and techniques used in Data Analytics. The statistical foundations will be covered first, followed by various machine learning and data mining algorithms. Technological aspects like data management, scalable computation and visualization will also be covered. In summary, this course will provide exposure to theory as well as practical systems and software used in data analytics. After completing this course, you will learn how to: Find a meaningful pattern in data Graphically interpret data Implement the analytic algorithms Handle large scale analytics projects from various domains Develop intelligent decision support systems

…Syllabus Data definition Descriptive Statistics Concept of data Data vs. Information Data categorization Descriptive Statistics Measure of central tendency Measure of location of dispersion Basic Analysis Techniques Statistical hypothesis generation and testing Chi-Square test t-Test, Analysis of variance, Correlation analysis Maximum likelihood test

…Syllabus Data Analysis Techniques Case Studies and Projects Regression analysis Classification techniques Clustering techniques Association rule analysis Case Studies and Projects Understanding few business scenarios Feature engineering and visualization Scalable and parallel computing with Hadoop and MapReduce Sensitivity analysis

…Study materials Probability & Statistics for Engineers & Scientists (9th Edn.), Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers and Keying Ye, Prentice Hall Inc. The Elements of Statistical Learning, Data Mining, Inference, and Prediction (2nd Edn.), Trevor Hastie Robert Tibshirani, Jerome Friedman, Springer, 2014 An Introduction to Statistical Learning: with Applications in R, G. James, D. Witten, T Hastie, and R. Tibshirani, Springer, 2013 Software for Data Analysis: Programming with R (Statistics and Computing), John M. Chambers, Springer, 2012 Mining Massive Data Sets, A. Rajaraman and J. Ullman, Cambridge University Press, 2012. Advances in Complex Data Modeling and Computational Methods in Statistics, Anna Maria Paganoni and Piercesare Secchi, Springer, 2013

…Study materials Lecture slides and other materials can be had at Data Mining and Analysis, Mohammed J. Zaki, Wagner Meira, Cambridge University Press, 2012 Hadoop: The Definitive Guide (2nd Edn.) by Tom White, O-Reilly, 2014 MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, Donald Miner, Adam Shook, O'Reilly, 2014 Beginning R: The Statistical Programming Language, Mark Gardener, Wiley, 2013 Lecture slides and other materials can be had at http://cse.iitkgp.ac.in/~dsamanta/

…Evaluation plan Minimum attendance required: 75% of the total classes Mid-Semester evaluation: 30% End-Semester evaluation: 40% Project-based evaluation: 30% (in four phases) Note: Minimum attendance and presence in all evaluations are must (other than some medical or emergency ground. No compensatory test or submission).

…Submissions of projects and assignments Moodle Course Management System https://10.5.18.110/moodle/ Steps to enroll to the course at Moodle Create your account (if it is not created earlier) User Id: <Roll Number> Password: Email account: Enrolment Key : STUDENT Verification your account, check the registered mail box Login to Moodle with “User Id” and “Password” Select the course “Data Analytics” from list of courses at the link “My Course”

…Doubt clearance and discussions Please use “Discussion Forum” at the link “Moodle” in the course web page at http://cse.iitkgp.ac.in/~dsamanta/courses/da/index.html

Happy Learning!