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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. Enrollment extended through February 1
- START DATE January 25, 2021
- 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.
How You Will Learn
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LEARN BY DOING
Practice processes and methods through simulations, assessments, case studies and tools, including the IBM Q experience.
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LEARN FROM OTHERS
Connect with an international community of professionals while working on projects based in real-world examples.
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LEARN ON DEMAND
Access all of the content online and watch videos on the go.
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REFLECT AND APPLY
Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
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DEMONSTRATE YOUR SUCCESS
Earn a Professional Certificate and 1.8 Continuing Education Units (CEUs) from MIT.
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LEARN FROM THE BEST
Gain insights from MIT faculty and industry experts.
What learners are saying
More than 7,000 professionals have completed this course.
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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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 the real world examples helped associate concepts with applications. I am now more aware and equipped compared to Day 1.
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 the means to. Now that I am equipped with new data science skills, I will be looking at new ways to manipulate and analyze various data sets within the organization.
MIT Faculty & Industry Experts
Meet your instructors
Devavrat Shah
Professor, Department of Electrical Engineering & Computer Science; Director, Statistics and Data Science Center
Philippe Rigollet
Associate Professor, Mathematics Department at MIT
Victor Chernozhukov
Professor, Department of Economics and the Statistics and Data Science Center at MIT
Stefanie Jegelka
Associate Professor, Department of Electrical Engineering & Computer Science and member of Computer Science and AI Lab and IDSS
Ankur Moitra
Associate Professor, Department of Mathematics and member of the Computer Science and Artificial Intelligence Lab at MIT
Tamara Broderick
Associate Professor, Department of Electrical Engineering & Computer Science and a member of the Computer Science and AI Lab at MIT
David Gamarnik
Nanyan Technical University Professor, Sloan School of Management
Jonathan Kelner
Associate Professor, Department of Mathematics and a member of the Computer Science and AI Lab at MIT
Kalyan Veeramachaneni
Principal Research Scientist at the Laboratory for Information and Decision Systems at MIT
Caroline Uhler
Associate Professor, Department of Electrical Engineering & Computer Science and IDSS
Guy Bresler
Associate Professor, Department of Electrical Engineering & Computer Science LIDS and IDSS
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