MIT course to smooth transition from big data to decisions

Ninety percent of the world’s data has been created in the past few years, and professionals are struggling to translate such data into actionable insights, according to organizers of the online course “Data Science: Data to Insights,” from MIT Professional Education. The six-week course begins October 4.

The course is a team effort developed in conjunction with MIT’s Institute for Data, Systems, and Society (IDSS) and will focus on topics including big-data interpretation, machine-learning best practices, and pattern identification.

Devavrat Shah, course co-director, said in a recent phone interview that a goal of the course is to help attendees go from data to decision. Classically, he said, this transition has occurred on a tiny scale. “What is needed now is a way to go from data to decision on a massive scale,” he said.

The course will be presented in five modules using a top-down rather than bottom-up approach, he said, allowing professional attendees to tackle challenges head-on. He added that one key aspect will be meeting the challenges of designing modern recommendation systems. Other modules will address unstructured data; regression and prediction; classification, hypothesis testing, and anomaly detection; and network and graphical models. Each module will include case studies.

The course outline’s “Who should participate” section specifically lists technical managers, business analysts, management consultants, and data scientists, but Shah said it would be a mistake for electrical and mechanical engineers to stay away.

The course complements an earlier MIT Professional Education course titled “Tackling the Challenges of Big Data,” which repeats beginning September 6. That course focused on infrastructure, with topics including database architectures, multicore processing, and sampling. Many infrastructure problems have been addressed over the last few years, Shah said, with professionals able to buy infrastructure as a service. The new course moves beyond the details of infrastructure implementation to putting it to use to reach decisions.

Shah is a professor in the MIT department of electrical engineering and computer science and a member of the Laboratory for Information and Decision Systems (LIDS), Computer Science and Artificial Intelligence Laboratory (CSAIL), and Operations Research Center (ORC). The other co-director, Philippe Rigollet, is an assistant professor in the MIT mathematics department and member of the Center for Statistics; he works at the intersection of statistics, machine learning, and optimization. Eight additional instructors will participate, providing insight on disciplines ranging from economics to management.

See these related posts on the earlier online course “Tackling the Challenges of Big Data”:

• Big data challenges: volume, velocity, variety
• Commuting via rapidly mixing Markov chains
• GPUs drive MIT’s MapD Twitter big-data application
• Emerging tools address data curation
• What is cloud computing?
• Big data impels database evolution
• Distributed computing platforms serve big-data applications
• NewSQL takes advantage of main-memory databases like H-Store
• Onions of encryption boost big-data security
• Lecture addresses scalability in multicore systems
• User interfaces can clarify, or obscure, big data’s meaning
• It’s a small world, but with big data
• Sampling, streaming cut big data down to size
• Coresets offer a path to understanding big data
• Machines learn from experience
• Machines read Twitter and Yelp so you don’t have to
• Computer scientists to transform medicine
• Got credit-card debt? Big data plots risk of default