Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.
In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications.
Dimitris Bertsimas is currently the Boeing Professor of Operations Research and a Co-Director of the Operations Research Center at MIT. He received his PhD in Applied Mathematics and Operations Research at MIT in 1988, and then joined the MIT faculty. His research interests include analytics and the applications of analytics in a variety of industries, including healthcare, finance, operations management and aviation. He is a member of the National Academy of Engineering, and he has received numerous research awards. Professor Bertsimas created the course 15.071 at MIT in 2008, and is currently working on an analytics textbook with colleagues Allison O’Hair and Bill Pulleyblank.
Allison O’Hair is currently a Lecturer of Operations Research and Statistics at the MIT Sloan School of Management. She received her PhD in Operations Research at MIT in 2013. Her research interests include applications of analytics and optimization in healthcare and other industries. Allison helped develop the course 15.071, and has served as a teaching assistant and lecturer for the course. She is currently working on an analytics textbook with colleagues Dimitris Bertsimas and Bill Pulleyblank.
John Silberholz is a PhD student in the MIT Operations Research Center. His research interests include the applications of analytics in the areas of healthcare decision making, bibliometrics, and heuristic design, and he applies concepts from 15.071 every day in his research. John took 15.071 in 2012 and was a teaching assistant for the course in 2013.
Iain Dunning is a PhD student in the MIT Operations Research Center. His research focuses on software and algorithms for optimization under uncertainty. Iain took 15.071 in 2012 and was a teaching assistant for the course in 2013.
Angie is a PhD student in the MIT Operations Research Center. Her research investigates real-world problems in need of operations research solutions. She has worked in areas as diverse as crime, transportation, marketing, and healthcare. She also works on improving the algorithms that underlie analytics techniques. She was a teaching assistant for 15.071 at MIT in 2014.
Velibor Misic is a PhD student in the MIT Operations Research Center. His research is in optimization and analytics. Before coming to MIT, he applied analytics methods to better design lung cancer radiotherapy treatments and total marrow irradiation treatments. Velibor took 15.071 in 2013 and was a teaching assistant for the residential version of 15.071 in 2014.
Nataly Youssef is a PhD student in the MIT Operations Research Center. Her research is in optimization under uncertainty and analytics with applications in supply chain management, data and call centers. Nataly took 15.071 in 2012 and enjoyed teaching regression and optimization for the Executive MBA class at MIT in 2013 and 2014.
Basic mathematical knowledge (at a high school level). You should be familiar with concepts like mean, standard deviation, and scatterplots. Mathematical maturity and prior experience with programming will decrease the estimated effort required for the class, but are not necessary to succeed.