Data Analytics

Announcements

Date	Message
11 November 2023	Announcement of Projects: Last date of submission: 01.12.2023 Project Allocation.
03 October 2023	The Attendance record till 27.09.2023 here.
18 September 2023	The Mid-Semester test of the course will be held on 26.09.2023 (FN). All students should check there allotted rooms and timimg from the ERP.
13 September 2023	The class test of the course will be held on 15.09.2023 in NR 221 from 16:30 hours. All students should occupy their seats latest by 16:15 hours.
16 August 2023	The class of the course on 17.08.2023 stands canceled.
10 August 2023	The class of the course on 10.08.2023 stands canceled.
04 August 2023	The course calendar is announced here.
02 August 2023	The first class of the course will be held on 04.08.2023.

Course Overview & Objectives

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 (Hadoop), scalable computation (MapReduce) 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

An outline of the course is as follows. You can also download the syllabus for your reference.

Data Definitions and Analysis Techniques

Elements, Variables, and Data categorization
Levels of Measurement
Data management and indexing
Introduction to statistical learning and R-Programming

Descriptive Statistics

Measures of central tendency
Measures of location of dispersions
Practice and analysis with R

Basic Analysis Techniques

Basic analysis techniques
Statistical hypothesis generation and testing
Chi-Square test
t-Test
Analysis of variance
Correlation analysis
Maximum likelihood test
Practice and analysis with R

Data analysis techniques

Regression analysis
Classification techniques
Clustering
Association rules analysis
Practice and analysis with R

Case studies and projects

Understanding business scenarios
Feature engineering and visualization
Scalable and parallel computing with Hadoop and Map-Reduce
Sensitivity Analysis

Resources & References

The following text and reference books may be referred to for this course.

Probability & Statistics for Engineers & Scientists (9^th 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 (2^nd 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
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
Data Mining and Analysis, Mohammed J. Zaki, Wagner Meira, Cambridge, 2012
Hadoop: The Definitive Guide (2^nd 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

Additionally, you may look at the following materials.

Course Plan

Introduction	Problmes for Practice
Data in Data Analytics	Problmes for Practice
Descriptive Statistics	Problmes for Practice
Statistical Methods	Problmes for Practice Tutorial-I
Statistical Learning	Problmes for Practice Tutorial-II Statistical Tables
Mid-semester Evaluation	Class Test-1 Paper Mid-semester Test Paper
Relation Analysis	Problmes for Practice Problmes for Practice
Analysis of Variance	Problmes for Practice
Bayesian Classifier
Support Vector Machine
Decision Tree Induction
Sensitivity Analysis
Tutorial-III End Semester Test 2020 End Semester Test 2021 End Semester Test 2022 Tutorial-IV

Course Coordinator: Prof. Debasis Samanta

Prof. Debasis Samanta is a Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur. For details about him, please see the link http://cse.iitkgp.ac.in/~dsamanta/

For any query, you can reach Dr. Samanta at:

+91-3222-282334 (Office)

+91-3222-282335 (Residence)

+91-7797137576 (Only SMS)

d...@iitkgp.ac.in

d...@gmail.com

Teaching Assistants

Subrata Pain (subratabankata@gmail.com)
Soham Bandyopadhyay (sohamban@gmail.com )
Nilanjan Sinhababu (nilanjan.thecseboy@gmail.com)

Moodle

Moodle, an online course management system, will be used extensively in this course. You should sign up for the course Moodle at the earliest. Once you click on the link, you would be redirected to the CSE home page where you would find a link for signing up at the bottom of the page.

In case of any doubt on the subject matter and topics covered in the class, you are welcome to participate in the Discussion Forum and post your query. We would get back to you with a response as soon as possible. Moreover, your friends can help answer your query too! Discussions in the forum may be moderated.

(CS61061)