Data Science and Big Data Analytics:
Making Data-Driven Decisions

Every day, your organization generates new data on your customers, your processes, and your industry. But could you be using your data more effectively? Discover how to turn big data into even bigger results in our seven-week digital course from MIT.

Download a FREE case study taken from this course! With this hands-on activity you’ll be able to build your own recommendation engine and get a sneak peek of the course.

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MAKE DATA-DRIVEN BUSINESS DECISIONS:

90% of the world’s data has been created in just the past few years. Faced with overwhelming amounts of data, organizations are struggling to extract the powerful insights they need to make smarter business decisions. To help uncover the true value of your data, MIT Institute for Data, Systems, and Society (IDSS) created the online course Data Science and Big Data Analytics: Making Data-Driven Decisions for data scientist professionals looking to harness data in new and innovated ways.

Over the course of seven weeks, you will take your data analytics skills to the next level as you learn the theory and practice behind recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, machine learning, and big data analytics.

Through in-depth lectures from renowned faculty from across MIT, you’ll acquire the theories, strategies, and tools you need to answer questions such as:

  • What is clustering and when should I use it?
  • What is the best way to design experiments and conduct hypothesis testing using my data?
  • How should I do model selection and avoid over-fitting?
  • What are the latest trends in machine learning?
  • How do graphical models and network models differ?

LEARNING OUTCOMES:

  • Apply data science techniques to your organization’s data management challenges.
  • Identify and avoid common pitfalls in big data analytics.
  • Deploy machine learning algorithms to mine your data.
  • Interpret analytical models to make better business decisions.
  • Understand the challenges associated with scaling big data algorithms.
  • Convert datasets to models through predictive analytics.

Ready to earn an MIT Professional Certificate in Data Science?

SEPTEMBER COURSE DETAILS:

Start Date: September 10, 2018
End Date: October 29, 2018
Duration: 7 Weeks
Time Commitment: 4-5 hours per week
Learning Format: Online
Price: $849

ENROLL NOW

OCTOBER COURSE DETAILS:

Start Date: October 15, 2018
End Date: December 10, 2018
Duration: 8 Weeks (1 holiday week)
Time Commitment: 4-5 hours per week
Learning Format: Online
Price: $849

ENROLL NOW

You might also be interested in the NEW MicroMasters credential in Statistics and Data Science

If you’re looking for a program that dives even deeper into all aspects of Data Science, take a look the MITx MicroMasters Program in Statistics and Data Science. Complete a set of 4 online graduate-level courses from MIT faculty, plus a virtually-proctored exam to earn your MicroMasters credential. This credential allows you to apply completed coursework to an MIT PhD or a Master’s (online or on-campus) at six other universities. Learn more.

COURSE HIGHLIGHTS

Learn online - when & where you would like.

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

Connect with an international community of professionals.

Learn from leading MIT faculty, industry experts, and business leaders.

Enjoy a robust collaborative environment to network and connect with fellow learners.

Video tutorials & research-based content from a host of MIT professors & industry experts.

MEET THE INSTRUCTORS

Co-Director, Statistics and Data Science Center (IDSS), Professor, Laboratory for Information and Decision Systems (LIDS), Computer Science and Artificial Intelligence Laboratory (CSAIL) and Operations Research Center (ORC) at MIT

Co-Director, Associate Professor, Mathematics department and Statistics and Data Science Center (IDSS) at MIT

Professor, Department of Economics; Statistics and Data Science Center (IDSS) at MIT

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

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

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

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

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

Principal research scientist at the Laboratory for Information and Decision Systems (LIDS) at MIT

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

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

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