DATA SCIENCE AND BIG DATA ANALYTICS

Making Data-Driven Decisions

   Self Paced

   7 Weeks

   54,000 (+Applicable Taxes)

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Overview

As the demand for new data is on the rise, there is a need to handle and analyze large datasets to get valuable business insights. An organization generates new data for its customers, processes, and industries on a day-to-day basis. The question is – how to use data effectively?

Learn the art of achieving tremendous results with big data in this seven-week online course and earn an MIT Certificate on Data Science as well as 1.8 Continuing Education Units (CEUs) upon completion.

What you’ll learn :

  • Data science techniques to manage the data management challenges of the organization
  • Identify and check common pitfalls in big data analytics.Data mining using machine learning algorithms
  • Make better business decisions by interpreting analytical models
  • Convert datasets to models through predictive analytics
  • Recognize the challenges associated with scaling big data algorithms
  • Understand how to represent the data when making predictions.

Who Should Participate

This course is designed for those who wish to turn big data into actionable insights. The course is well suited for data scientists, data analytics, early-career aspirants and experienced professionals. As is not an introductory course, the participants should have a substantial background knowledge of statistical techniques and data calculations or quantitative methods of data research.

Participants may include:

  • Technical managers
  • Business intelligence analysts
  • Management consultants
  • IT practitioners
  • Business managers
  • Data science managers
  • Data science enthusiasts

 

 

Job Outlook

“Leveraging this knowledge will allow me to position myself as a hybrid analyst-data scientist, which greatly increases my value to the company.” 
 

- Ryan Michael Dickinson

“I really enjoyed the interactions/animations in the videos. These really helped with visualizing the concepts… I feel more equipped to understand what type of insights can be gleaned from a particular set of data, and can better communicate these asks to our data science team.”

- Reza Dawood

“The course contentwas really amazing and gave me exact direction to head towards the Big Datatopic.”
 

- Prasad Sankpal

“It's very critical to keep acquiring new knowledge in today's ever changing landscape of both world order and opportunities available to professionals.”
 

- Joanna Zarach

“The quality and pace of the videos and material is top-notch. I really like having different instructors for different modules and having two instructors interacting together makes the material more vivid and entertaining.”

- Miguel Hurtado

“Armed with the knowledge I have gained from his course, I can introduce my team to certain methods that can be applied to our day to day work.”
 

– Anonymous Learner

Syllabus

To achieve the best learning objectives, the course material blends the following pedagogical strategies: 

Instructivism: Teacher-centered learning where the instructors present relevant content (tutorial videos enhanced with animation and graphics). Students will test their knowledge through graded tests.

Constructivism: Learning by doing approach. We encourage learners to construct their own understanding through solving the mandatory and optional case studies and practicing.

Social constructivism: Learning through social interactions and communication. You will be able to discuss with your peers in the discussion groups, and evaluate and get reviews from your peers through two compulsory case studies.

Connectivism: Connecting with others and extending your knowledge through communication. You will be able to expand and share your knowledge with others through the Discussion group, and course groups on Facebook, and LinkedIn.

Module 1: Making sense of unstructured data

Module 2: Regression and Prediction

Module 3: Classification, Hypothesis Testing and Anomaly Detection

Module 4: Recommendation Systems

Module 5: Networks and Graphical Models

Module 6: Predictive Modeling for Temporal Data

Program and Payment Details


Course Start
 

3rd February, 2020 

Self Paced, 7 Weeks


Time Commitment
 

4-5 Hours

Per Week


Program Fees
 

₹ 54,000

(+ Applicable Taxes)

 

 


Payment: Debit / Credit Card
 

Loan Facility Available

Instructor

Devavrat Shah, Course Co-Director

Director, Statistics and Data Science Center (SDSC); Professor, Electrical Engineering and Computer Science; Member, Laboratory for Information and Decision Systems (LIDS), Computer Science and Artificial Intelligence Laboratory (CSAIL), and Operations Research Center (ORC)

 

Philippe Rigollet, Course Co-Director

Associate Professor, Mathematics department and Statistics and Data Science Center (SDSC)

Guy Bresler

Assistant Professor, Electrical Engineering and Computer Science, LIDS and IDSS

Tamara Broderick

Assistant Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science Department (EECS)

Victor Chernozhukov

Professor, Department of Economics; Statistics and Data Science Center (SDSC)

 

David Gamarnik

Professor, Sloan School of Management, IDSS, and the Operations Research Center

Stefanie Jegelka

Associate Professor, Department of Electrical Engineering and Computer Science, member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS)

Jonathan Kelner

Professor, Department of Mathematics and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)

Ankur Moitra

Associate Professor, Department of Mathematics and member of the Computer Science and Artificial Intelligence Lab (CSAIL)

 

Caroline Uhler

Associate Professor, Institute for Data, Systems, and Society (IDSS), Electrical Engineering and Computer Science Department (EECS)

Kalyan Veeramachaneni

Principal Research Scientist, MIT Laboratory for Information and Decision Systems (LIDS)