Data Science course with MIT is aimed at teaching its participants theories, strategies, and tools that are need to convert gigabytes of data into meaningful insights. Over the course of six weeks, topics including recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, and machine learning will be discussed. Using case studies and hand-on exercises, participants will practice and increase their data analysis skills. After completing this course,participants will be well prepared to: 1.Uncover unexpected patterns and anomalies in your data 2.Determine what data you need and how to design experiments 3.Use foundational and emerging analytics techniques 4.Understand common pitfalls in big data analytics and how to avoid them 5.Comprehend how machine learning works in practice 6.Interpret model results and make more effective decisions 7.Overcome the challenges and constraints associated with scaling big data algorithms

1. Bhaskar Pant
Executive Director
MIT Professional Education
Devavrat Shah
Professor & Director of Statistics and Data Science Center
MIT, EECS, IDSS, LIDS, Statistics and Data Science Center
Philippe Rigollet
Associate Professor
MIT, Department of Mathematics, IDSS, LIDS, Statistics and
Data Science Center and Broad Institute
Massachusetts Institute of Technology
This is to certify that
Abhishek Maligehalli Shivalingaiah
has successfully completed
Data Science: Data to Insights
February 21 – April 4, 2017