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

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

3. …Course organization Title: Data Analytics Code: CS40003
Credit: = 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

4. …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

5. …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

6. …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

7. …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

8. …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

9. …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).

10. …Submissions of projects and assignments
Moodle Course Management System
Steps to enroll to the course at Moodle
Create your account (if it is not created earlier)
User Id: <Roll Number>
Password:
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”

11. …Doubt clearance and discussions
Please use “Discussion Forum” at the link “Moodle” in the course web page at

12. Happy Learning!