Course Name: Introduction to Data Analytics

Course abstract

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 broad and grand objective, the last 20 years has seen steeply decreasing costs to gather, store, and process data, creating an even stronger motivating 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.


Course Instructor

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Prof. Nandan Sudarsanam

Prof. 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
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Prof. Balaraman Ravindran

Prof. 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
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Course Sponsors

Teaching Assistant(s)

VAISHNAVI MURALIDHARAN

IIT Madras

 Course Duration : Jul-Sep 2015
  View Course
 Syllabus
 Enrollment : 18-May-2015 to 12-Jul-2015
 Exam registration : 01-Jul-2015 to 15-Aug-2015
 Exam Date : 06-Sep-2015, 13-Sep-2015

Enrolled

7524

Registered

498

Certificate Eligible

392

Certified Category Count

Gold

13

Elite

232

Successfully completed

102

Participation

45

Success

Elite

Gold




Legend

>=90 - Elite+Gold
60-89 - Elite
35-59 - Successfully Completed
<=34 - Certificate of Participation

Final Score Calculation Logic

  • Assignment Score = 50% of best 3 out of 5 assignments
  • Exam Score = 50% of Certification Exam Score
  • FINAL SCORE (Score on Certificate) = Exam Score + Assignment Score.
Introduction to Data Analytics - Toppers list

AKANSHA KHANNA 93%

IBM INDIA PVT LTD

DINKER GEORGE MATTAM 93%

DECIMAL POINT ANALYTICS

PARUL MADAAN 92%

PITNEY BOWES SOFTWARE INDIA PVT LTD

RAHUL KUMAR 92%

INDIAN INSTITUTE OF MANAGEMENT, KOZHIKODE

PADMASUNDARI G 91%

INDIAN INSTITUTE OF TECHNOLOGY, MADRAS

PARAG JAIN 91%

PEC University of Technology

VISHAL PAGIDIPALLY 91%

IIT MADRAS

ABHISHEK BHUPINDER SAINI 90%

IIT MADRAS

M SANJAY KUMAR 90%

IIT MADRAS

KAMMULA RUPADITYA 90%

IIT MADRAS

SWARNA GUPTA 90%

HEXAWARE TECHNOLOGIES

RAJARSHI BANERJEE 90%

ASIAN PAINTS MIDDLE EAST LLC

HARISH JETHANANDANI 90%

NEELKANTH PUBLISHERS JAIPUR

Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.