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Courses » Introduction to Data Analytics

Introduction to Data Analytics

“We are drowning in information and starving for knowledge”

-- Rutherford D. Roger

Data Analytics is the science of analyzing data to convert information to useful knowledge. This knowledge could help us understand our world better, and in many contexts enable us to make better decisions. While this is the broad and grand objective, the last 20 years has seen steeply decreasing costs to gather, store, and process data, creating an even stronger motivation for the use of empirical approaches to problem solving. This course seeks to present you with a wide range of data analytic techniques and is structured around the broad contours of the different types of data analytics, namely, descriptive, inferential, predictive, and prescriptive analytics.

7161 students have enrolled already!!

COURSE INSTRUCTORS:



Dr. Nandan Sudarsanam holds a Ph.D. in Engineering Systems from Massachusetts Institute of Technology (MIT). His research interests and work experience spans the areas of Data mining/ Machine learning, Experimentation, Applied Statistics, and Algorithmic approaches to problem solving. Dr. Nandan currently works as a faculty member at the Department of Management Studies at IIT-Madras.

 

Dr. Balaraman Ravindran completed his Ph.D. at the Department of Computer Science, University of Massachusetts, Amherst. He worked with Prof. Andrew G. Barto on an algebraic framework for abstraction in Reinforcement Learning. Dr. Ravindran’s current research interests spans the broader area of machine learning, ranging from Spatiotemporal Abstractions in Reinforcement Learning to social network analysis and Data/Text Mining.

PRE- REQUISITES:

This course requires that you are familiar with high-school level linear algebra, and calculus. Knowledge of probability theory, statistics, and programming is desirable.

COURSE SYLLABUS

We will have a total of 8 weeks for this course. Every week we will have between 3-7 video lectures ranging from 10-60 minutes each. There will be a total of 150 instructional minutes (2 hours and 30 minutes) per week. There will be one assignment at the end of every week for a total of 8 assignments.

CERTIFICATION EXAM

The exam is optional.
Exams will be on 6 September 2015 and 13 September, 2015.
Time: 1pm-4pm
The list of cities where the exam will be conducted will be available in the registration form.

Registration URL: Announcements will be made when the registration form is open for registrations, most likely in July 2015. The online registration form has to be filled and the certification exam fee of Rs 1000 needs to be paid.

CERTIFICATE

Certificate will be given to those who register and write the exam. Certificate will have your name, photograph and the score in the final exam. It will also have the logos of NPTEL and IIT Madras. It will also be e-verifiable on the nptel.ac.in/noc website.

SYLLABUS OUTLINE

Week

Contents

1

Descriptive Statistics 

Introduction to the course
Descriptive Statistics
Probability Distributions

2

Inferential Statistics
Inferential Statistics through hypothesis tests

3

Regression & ANOVA
Regression
ANOVA(Analysis of Variance)

4

Machine Learning: Introduction and Concepts
Differentiating algorithmic and model based frameworks
Regression : Ordinary Least Squares, Ridge Regression, Lasso Regression,
K Nearest Neighbours Regression & Classification

5

Supervised Learning with Regression and Classification techniques -1
Bias-Variance Dichotomy
Model Validation Approaches
Logistic Regression
Linear Discriminant Analysis
Quadratic Discriminant Analysis
Regression and Classification Trees
Support Vector Machines

6

Supervised Learning with Regression and Classification techniques -2
Ensemble Methods: Random Forest
Neural Networks
Deep learning

7

Unsupervised Learning and Challenges for Big Data Analytics
Clustering
Associative Rule Mining
Challenges for big data anlalytics

8

Prescriptive analytics
Creating data for analytics through designed experiments
Creating data for analytics through  Active learning
Creating data for analytics through Reinforcement learning


REFERENCE

[1]Hastie, Trevor, et al. The elements of statistical learning. Vol. 2. No. 1. New York: springer, 2009.

[2] Montgomery, Douglas C., and George C. Runger. Applied statistics and probability for engineers. John Wiley & Sons, 2010


Click here to download the pdf copy of the syllabus.