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Courses » Introduction to Data Analytics

Introduction to Data Analytics

ABOUT THE COURSE

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.


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.

7860 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.

COURSE LAYOUT

Week 1
Descriptive Statistics
Introduction to the course
Descriptive Statistics
Probability Distributions

Week 2
Inferential Statistics
Inferential Statistics through hypothesis tests

Week 3
Regression & ANOVA
Regression
ANOVA(Analysis of Variance)

Week 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

Week 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

Week 6
Supervised Learning with Regression and Classification techniques -2
Ensemble Methods: Random Forest
Neural Networks
Deep learning

Week 7
Unsupervised Learning and Challenges for Big Data Analytics
Clustering
Associative Rule Mining
Challenges for big data anlalytics

Week 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.


CERTIFICATION EXAM

  • The exam is optional for a fee. Exams will be on 24 Sep, 2017
  • Time: Shift 1: 9am-12 noon; Shift 2: 2pm-5pm
  • Any one shift can be chosen to write the exam for a course.
  • Registration url: Announcements will be made when the registration form is open for registrations.
  • The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published.
CERTIFICATE
  • Final score will be calculated as : 25% assignment score + 75% final exam score
  • 25% assignment score is calculated as 25% of average of best 6 out of 8 assignments
  • E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of NPTEL and IIT Madras.It will be e-verifiable at nptel.ac.in/noc.