Take a sneak peek at the case study used in this course and learn to build your own recommendation engine. Complete the form below:
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Could you be using data more effectively?
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 innovative 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.
At the end of this course you will receive a digital Professional Certificate and 1.8 Continuing Education Units (CEUs) from MIT.
MIT Faculty explain the impact of big data on business decision making.
We bring together an innovative pedagogy paired with world-class faculty.
Practice processes and methods through simulations, assessments, case studies, and tools.
Connect with an international community of professionals while working on projects based on real-world examples.
Access all of the content online and watch videos on the go.
Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
Earn a Professional Certificate and 1.8 Continuing Education Units (CEUs) from MIT.
Gain insights from leading MIT faculty and industry experts.
"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 this topic."
"I am very pleased with the course content, it is exactly the level I am looking for. Each professor/course presenter has packed a lot of information and has explained complex algorithms in good detail. Some with [a] good sense of humor."
"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."
"I had very basic information about data sciences and machine learning when I started this course. This course has definitely helped in getting an overview of different aspects and algorithms for data analysis and processing and to gather insights out of it."
Professor, Department of Electrical Engineering & Computer Science; Director, Statistics and Data Science Center
Associate Professor, Mathematics Department at MIT
Professor, Department of Economics and the Statistics and Data Science Center at MIT
Associate Professor, Department of Electrical Engineering and Computer Science and member of Computer Science and AI Lab and IDSS
Associate Professor, Department of Mathematics and member of the Computer Science and AI Lab at MIT
Associate Professor, Department of Electrical Engineering and Computer Science and a member of the Computer Science and AI Lab at MIT
Nanyan Technological University Professor, Sloan School of Management
Associate Professor, Department of Mathematics and a member of the Computer Science and AI Lab at MIT
Principal Research Scientist at the Laboratory for Information and Decision Systems at MIT
Associate Professor, Department of Electrical Engineering and Computer Science at MIT and IDSS
Associate Professor, Department of Electrical Engineering & Computer Science, Laboratory for Information and Decision Systems and IDSS
Professionals at any career stage, looking to turn large volumes of data into actionable insights.
Past learners' job roles have included: business intelligence analysts, management consultants, technical managers, business managers, data science managers.
Data science enthusiasts and IT professionals.
Background knowledge of statistical techniques and data calculations or quantitative methods of data research is strongly recommended.
Familiarity with either R or Python is recommended but not required.
Ever wonder how companies like Netflix, Spotify and Pandora filter products based on their unique user’s preference?
In this case study, you will learn how Netflix utilizes Recommendation Engines to provide the best possible shows and movies for unique users.
To access this case study, submit your information in the form above.
