Big Data and Social Analytics certificate course

Course starts: 7 November 2016
Registrations close: 1 November 2016
Course fees: $2,300

Earn a certificate from the Massachusetts Institute of Technology

MIT's mandate is to advance knowledge in areas that will serve to address the 21st century's great challenges by educating students in fields of scholarship that will best serve the world. To this end, MIT’s Experimental Learning (MIT XL) is collaborating with online education leaders, GetSmarter, to present this course entirely online and part-time.

Why this course?

Imagine what you could learn about humanity, and the way we interact not only with technology, but also each other, given the amount of data available to be analyzed.

Social physics - a key focus of this course - is described by Professor Alex Pentland, one of the world’s leading data scientists, as “statistics meets big data to understand people”. It unlocks a whole new way to think about big data analytics by shifting focus to how the results of data analysis not only contribute to better decision making, but also how they can better assist humanity.

These analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations as well as a range of other business benefits.

Course overview

Designed for technically-minded professionals from any industry, this online course will equip you with the fundamental theoretical knowledge and practical abilities needed to analyze big data to better understand and predict human networks and behaviors in social structures.

Over the course of eight weeks, you will be guided in how to use the technical tools, datasets and code scripts associated with big data analysis, including the likes of Jupyter Notebook, Funf and Bandicoot.

While a background in statistics and/or Python programming is not a requirement, it is recommended that you’ve had at least some basic exposure to the Python programming language or similar, as all students will engage with the technical elements of this course. You will not, however, be asked to write advanced code from scratch. Although you will be asked to write simple code at times, the majority of the course allows you to modify pre-written scripts and examine the effect of those minor adjustments.

Is this course for you?

This course is suitable for technically-minded graduates and working professionals in any role, across any industry. Whether you work with the technical aspects of data analysis in your role, or you manage a team of technical professionals, this course is designed to accommodate varying Python proficiency levels through both compulsory core activities and optional advanced ones.

This course is for you if you want to become familiar with the technical and theoretical aspects of big data social analytics, but not if you are already a well-established social analytics data scientist. Specific roles that would benefit include, but are not limited to: Analysts and Analytics Managers; Consultants; Software Engineers, Developers and Programmers; Enterprise Architects and other systems specialists; Directors with data-intensive portfolios and CEOs, especially those in the IT industry; Data Scientists and Engineers looking to transition into such a role; and Researchers and Project Managers who work with large datasets.

Regardless of your industry or profession, you’ll walk away from this course confident in your ability to: understand exactly what kind of data you are dealing with in your role; conduct preliminary analysis and draw hypotheses about that data; and design interventions using that analysis that are intended to change behavior.

What you will learn

  • Module 1: Foundations of big data and social physics
  • Module 2: Personal sensors and human behavior
  • Module 3: First-order analysis and data exploration
  • Module 4: Networks of physical interactions
  • Module 5: Second-order analysis and data exploration
  • Module 6: Using data to affect behavior change
  • Module 7: Application of big data in industry
  • Module 8: Data in action

The course starts with an Orientation period of 10 days to give students a chance to familiarise themselves with the course structure and requirements, explore the Online Campus, and meet peers on the class discussion forum. After Orientation, 8 academic content modules are released weekly every Wednesday.

Download the info pack for a detailed breakdown of each module's content.

MIT Instructors

Alex “Sandy” Pentland is founding faculty director of the MIT Connection Science Research Initiative, which uses network science to access and change real-world human behavior, and holds a triple appointment at MIT in Media Arts and Sciences, Engineering Systems Division and with the Sloan School of Management. Sandy is a founding member of advisory boards for Google, AT&T, Nissan, and the UN Secretary General, and a serial entrepreneur who has co-founded more than a dozen companies, including two hedge funds. Sandy currently advises on data/analytics to the UN Secretary General and the boards of AT&T, Google, Telefonica and others.



Managing Director of MIT Connection Science

Dave leads new initiatives for MIT, advises the European Commission on commercializing innovation and building regional innovation capacity, and counsels leadership at private and public companies on growth strategies.



Research Scientist at MIT Media Lab

Yves-Alexandre recently received his PhD in computational privacy from MIT. His research on the shortcomings of anonymization has appeared in reports of the World Economic Forum, European Commission, and the OECD. Yves-Alexandre worked for the Boston Consulting Group and acted as an expert for both the Bill and Melinda Gates Foundation and the United Nations.



Visiting Researcher at MIT Media Lab

Arek is currently a Data Scientist working in People Analytics, and a Postdoctoral Fellow at both the Technical University of Denmark and at MIT Media Lab (Human Dynamics Group). Arek is particularly interested in mobile technologies, and how they can be used to learn more about human beings.



Postdoctoral Fellow in the Human Dynamics Group at the MIT Media Lab

His research focuses on emerging signal processing and machine learning techniques on graphs, and their applications to the understanding of human behavior, decision making and societal changes.Prior to joining MIT, he received his PhD degree in Signal Processing from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.



Public policy lawyer at Sidley Austin, and speaker and writer on Public Policy & Technology at Brookings Institution and MIT

Cam Kerry applies his experience as a government thought leader on technology and public policy to current issues in these areas. His work focuses especially on privacy and information security, and the application of privacy principles to fast-changing global business and technology. He is the Former General Counsel for the U.S. Department of Commerce.