Data Science and Big Data Analytics: Making Data Driven Decisions

Turn big data into even bigger results with a seven-week online course from MIT

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  • START DATE February 3, 2020
  • TIME COMMITMENT 4-5 hours per week
  • DURATION 7 weeks
  • FORMAT Online
  • PRICE $899

WHAT YOU WILL LEARN

Could you be using data more effectively? Uncover your data’s true value and learn how to leverage it with the latest and most powerful tools, techniques, and theories in data science from industry experts and renowned MIT faculty. View the week to week course schedule here.

  • Apply data science techniques to your organization’s data management challenges.
  • Explore the latest trends in machine learning.
  • Convert datasets to models through predictive analytics.
  • Improve your business decision-making using analytical models.
  • Identify and avoid common pitfalls in big data analytics.
  • Deploy machine learning algorithms to mine your data.
  • Master best practices for experiment design and hypothesis testing.
  • Determine the difference between graphical models and network models.
  • Choose how to represent your data when making predictions.

Who Should Enroll

  • Professionals at any career stage, looking to turn large volumes of data into actionable insights.

  • Past learner job roles have included: business intelligence analysts, management consultants, technical managers, business managers, data science managers.

  • Data science enthusiasts and IT professionals.

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How You Will Learn

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    Worried about work/life balance?

    Learn online - when and where you would like.

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    WANT A CONFIRMATION OF SUCCESS?

    Earn a Professional Certificate and 1.8 Continuing Education Units (CEUs) from MIT.

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    Need a network?

    Connect with an international community of professionals.

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    Looking to innovate?

    Gain insights from leading MIT faculty, industry experts, and business leaders.

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    Ready for feedback

    Benefit from a robust, collaborative learning environment.

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    Interested in the latest trends?

    Access cutting edge, research-based content developed by MIT professors and industry experts.

What learners are saying

More than 7,000 professionals have completed this course.

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Murali Thyagarajan, DBA & Application Support, NASDAQ

The course was easy to understand and had depth. All the concepts were clearly laid out and explained. This is the best course I have come across on …

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Adnan Raza, Dir. Lead Business Consulting, Mackenzie Investments

As a novice student in the field of AI and Machine learning, the module based approach really helped me structure the step-wise approach. In addition…

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Linnea Serzan, BPO Manager at Johnson & Johnson

My newly gained knowledge from this course will help me organize drive insights from data that we previously were not modeling or analyzing, or had t…

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MIT Faculty & Industry Experts

Meet your instructors

Devavrat Shah

Devavrat Shah

Professor, Department of Electrical Engineering & Computer Science; Director, Statistics and Data Science Center

Philippe Rigollet

Philippe Rigollet

Associate Professor, Mathematics Department at MIT

Victor Chernozhukov

Victor Chernozhukov

Professor, Department of Economics and the Statistics and Data Science Center at MIT

Stefanie Jegelka

Stefanie Jegelka

Associate Professor, Department of Electrical Engineering & Computer Science and member of Computer Science and AI Lab and IDSS

Ankur Moitra

Ankur Moitra

Associate Professor, Department of Mathematics and member of the Computer Science and Artificial Intelligence Lab at MIT

Tamara Broderick

Tamara Broderick

Associate Professor, Department of Electrical Engineering & Computer Science and a member of the Computer Science and AI Lab at MIT

David Gamarnik

David Gamarnik

Nanyan Technical University Professor, Sloan School of Management

Jonathan Kelner

Jonathan Kelner

Associate Professor, Department of Mathematics and a member of the Computer Science and AI Lab at MIT

Kalyan Veeramachaneni

Kalyan Veeramachaneni

Principal Research Scientist at the Laboratory for Information and Decision Systems at MIT

Caroline Uhler

Caroline Uhler

Associate Professor, Department of Electrical Engineering & Computer Science and IDSS

Guy Bresler

Guy Bresler

Associate Professor, Department of Electrical Engineering & Computer Science LIDS and IDSS

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