Big Data and Social Analytics certificate course

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1 MASSACHUSETTS INSTITUTE OF TECHNOLOGY Big Data and Social Analytics certificate course Online and part-time PAGE 1 Massachusetts Institute of Technology School of Architecture + Planning

2 Ninety percent of all currently available data, globally, has been generated over the last two years. 1 By simply moving through the daily motions of our modern lives, we are all producing digital traces about our interactions with both technology and each other - making it possible for large-scale, high-impact social research to be conducted by those equipped with the skills and expertise needed to dissect that data. Personal privacy can be preserved in a fashion that nonetheless allows data to be harnessed for better commutes to work, better management of health, better access to financial resources, and other positive societal improvements. Businesses worldwide have realized that harnessing, processing, and analyzing the digital information produced by individuals, organizations and institutions should be a fundamental starting point for intelligent decision-making. Considering that only 0.5% of all currently available data is analyzed and used, there s a massive opportunity waiting to be taken. MIT Big Data and Social Analytics certificate course This 8-week online certificate course from the Massachusetts Institute of Technology (MIT) will cover the fundamental theory and analysis of big data to better understand and predict human networks and behaviours in social structures. Over the course of eight weeks, you will be guided in how to use the technical tools, data sets and code scripts associated with big data analysis. You will also have the opportunity to explore the unique open-source toolkits that MIT Connection Science has incubated (such as Funf and Bandicoot); discover novel applications relating to a new computational social science ( social physics ) that have proven ability to deliver behavior change at scale; and engage with key concepts around the ethics of data, personal privacy, and current trends in the field of data science. Who should take this course? Given the fact that by the year 2020, about 1.7 megabytes of new information will be created every second for every person on the planet, and over 73% of organizations have already invested or plan to invest in big data by the end of 2016, the relevance of this course is near universal. 2 The course is designed to adapt to your level of prior engagement with data knowledge by offering both core and extension activities. The course is suitable for technically-minded graduates and working professionals in any role, across any industry. 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. 1 source 2 source PAGE 2

3 At a glance: the MIT Big Data and Social Analytics online certificate course fact sheet 8 weeks, online 8-12 hours per week $2,300 Starts 11 July 2016 Payment options: PAY IN FULL: $2,300 3-PART PAYMENT PLAN: First installment: $1,500 Second installment: $500 Final installment: $500 Please note that an admin fee is charged on the 3-part payment plan Prerequisites: While a background in statistics and/or Python programming will be beneficial, it is not a requirement, as this course offers both core and extension activities that cater to entrylevel and more advanced students. Recommended reading: Social Physics: How Social Networks Can Make Us Smarter, Alex Pentland Outcome: Students who achieve a final mark of 70% or more will earn an MIT certificate in Computational Data Analysis. Key focus areas: Social science influence and application Data processing and analysis Data ethics and personal privacy Practical application of big data insights PAGE 3

4 MIT instructors on this course Alex Sandy Pentland 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. Currently advises on data/analytics to the UN Secretary General and the boards of AT&T, Google, Telefonica and others. Dave Shrier Managing Director of MIT Connection Science. Dave leads other 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. He has also driven over $8.5 billion of growth initiatives for various Fortune 1000 companies, and has served as CEO or COO/CFO for 6 privately funded companies. Yves-Alexandre de Montjoye Postdoctoral researcher in computational privacy at Harvard IQSS currently working with Professor Latanya Sweeney and Professor Gary King. Yves-Alexandre received his PhD at the MIT Media Lab under the supervision of Prof. Alex Sandy Pentland, and his research aims at understanding how the unicity of human behavior impacts the privacy of individuals in large-scale metadata datasets. Xiaowen Dong Postdoctoral Fellow in the Human Dynamics Group at MIT Media Lab. Xiaowen s 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. PAGE 4

5 Cam Kerry Public policy lawyer at Sidley Austin, and speaker and writer on Public Policy & Technology at Brookings Institution and MIT, where he applies his experience as a government thought leader on technology and public policy to current issues in these areas. He is the former General Counsel for the U.S. Department of Commerce. His work focuses especially on privacy and information security, and the application of privacy principles to fast-changing global business and technology. Arek Stopczynski Visiting Researcher at MIT Media Lab, 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. Over and above your video lectures from these MIT instructors, you ll also engage with your course material and fellow students via intensive, tutor-led small groups and your online discussion forum. You ll receive support from your dedicated Performance Coach throughout to keep you on track with your studies. Understanding these human-machine systems is what s going to make our future social systems stable and safe. We are getting beyond complexity, data science and web science, because we are including people as a key part of these systems. That s the promise of Big Data, to really understand the systems that make our technological society. As you begin to understand them, then you can build systems that are better. ALEX SANDY PENTLAND - FOUNDING FACULTY DIRECTOR OF MIT CONNECTION SCIENCE; COURSE INSTRUCTOR PAGE 5

6 What you will learn on this course Find out more about the learning outcomes you will achieve as you progress through the 8 modules of this course: ORIENTATION MODULE: WELCOME TO THE VIRTUAL LEARNING ENVIRONMENT Get to grips with your new online classroom. Meet your Course Teaching Team and get to know your fellow classmates. MODULE 1: BIG DATA AND SOCIAL PHYSICS Learn about groundbreaking theories relating to social physics and their use for predicting and influencing human networks and behaviors. Explore the fundamental theories related to big data processing, machine learning and statistics. Understand the typical sources of data, as well as the basics of data quality, data hygiene and data dynamics. Learn how to navigate the virtual analysis environment. MODULE 2: SOURCES OF DATA Understand human interactions and social physics through the lens of communication streams, social cues and Sandy Pentland s pioneering work on honest signals. Discover the types of data that can be collected through personal sensors and how data can be processed to create high-level features of human behavior. Explore the Funf Open Sensing Framework engage with practical learning exercises in data visualization. MODULE 3: FIRST-ORDER ANALYSIS AND DATA EXPLORATION Investigate key concepts relating to data quality, data hygiene and data dynamics. Revisit basic statistical modelling and analysis methods, and receive an introduction to visual data interrogation methods. Practically manipulate and explore data using Python as a simple first-cut analysis. MODULE 4: PEER NETWORKS Engage with basic concepts of peer networks and network theory. Demonstrate the ability to visually represent and measure peer networks. Apply graph partitioning algorithms to real world data sets. MODULE 5: SECOND-ORDER ANALYSIS AND DATA EXPLORATION Progress to second-order analysis and discover the difference between correlation and causation, as well as techniques for differentiating between them. Investigate concepts such as data deserts, data mirages and the data deluge, and discover how these concepts can assist in understanding the challenges of tackling big data. Find out about the different phases in the analytical approach. Explore the Bandicoot open source Python toolbox and discover how to leverage Bandicoot behavioral indicators. PAGE 6

7 MODULE 6: USING DATA TO EFFECT BEHAVIOR CHANGE Explore data ethics and considerations around identity, security and privacy. Understand the basics of data protection, along with historical data protection models, legal requirements, as well as newlyestablished data protection models, with a focus on location and transactional data. Design interventions to be taken as a result of big data analysis, and execute data masking practices. MODULE 8: DATA IN ACTION Examine problem set examples that look at the application of data in context and showcase data policy in action. Investigate the concept of a living lab as an experiential environment for exploring the use of big data. Recommend interventions to be taken as a result of your analysis in module 7, and present your final analysis and recommendations in an appropriate format. MODULE 7: APPLICATION OF BIG DATA IN INDUSTRY Investigate problem set examples that cover application of big data insights in Healthcare, HR tech, Telco and Marketing. Learn about fascinating use case examples, such as: > research from MIT that was able to predict within 70% accuracy whether a specific area in London would be a crime hotspot using anonymized human behavioral data; > the use of mobile phone data to model the spread of malaria, and > using mobility patterns of purchasing behavior to predict financial behavior and risk profiles. Combine what you have learned in modules 1 to 6 to conduct a full cycle analysis on a large data set. PAGE 7

8 A scaffolded, streamlined approach to speak to all levels of experience The course offers core activities and extension activities: the core activities can be completed by students with little to no Python programming experience. The extension activities are aimed at students with proficiency in Python programming, allowing for exploration of more sophisticated methods of big data analysis. This scaffolded experience will support non-technical students to answer research questions, test hypotheses, and generate results that can be interpreted for strategic application. Students with more advanced proficiency in Python programming and handling large data sets will have the opportunity to explore more sophisticated methods of big data analysis. Analysis of Big Data is increasingly about finding connections: connections with the people around you, and connections between people s behavior and outcomes. You can see this in all sorts of places. For instance, one type of Big Data and connection analysis concerns financial data. Not just the flash crash or the Great Recession, but also all the other sorts of bubbles that occur. What these are, are systems of people, communications, and decisions that go badly awry. Big Data shows us the connections that cause these events. Big data gives us the possibility of understanding how these systems of people and machines work, and whether they re stable. ALEX SANDY PENTLAND - FOUNDING FACULTY DIRECTOR OF MIT CONNECTION SCIENCE; COURSE INSTRUCTOR PAGE 8

9 What is MIT Experimental Learning? MIT Experimental Learning (MIT XL) is a new program within MIT s School of Architecture + Planning (home of the world famous MIT Media Lab). MIT XL is dedicated to providing actionable insights and hands-on capabilities enhancement for the working professional, incorporating MIT s latest thinking on computational social science and applied cognitive science to enhance the learning experience. Drawing from MIT s rich resources in innovation, technology research, design thinking, and systems thinking, MIT XL seeks to deliver a world-class learning experience to a global community. MIT XL incorporates tools and content from MIT Connection Science, a research-led initiative that uses data/analytics to build better organizations and better societies. The MIT School of Architecture + Planning is collaborating with GetSmarter to design, develop and deliver non-credit-bearing online certificate courses in both Future Commerce and Big Data and Social Analytics. Who is GetSmarter? GetSmarter is an online education company that collaborates with top-tier universities to present career-focused online short courses. Their people-driven approach to online learning has resulted in an average course completion rate of over 90% across a portfolio of over 60 university-approved short courses, over 8 years. How you ll learn Every course is broken down into manageable, weekly modules, designed to accelerate your learning process through diverse learning activities: Work through your instructional material online. Collaborate with your classmates on your projects via the discussion forum and online hangouts. Enjoy a wide range of interactive content, including video lectures, live polls, scenario simulations, and more. Investigate current, real-world case studies. Apply what you learn each week in quiz assessments, collaborate activities, and capstone project work. PAGE 9

10 MASSACHUSETTS INSTITUTE OF TECHNOLOGY Big Data and Social Analytics certificate course REGISTER NOW Contact us Call: PAGE 10 Massachusetts Institute of Technology School of Architecture + Planning