MITx: 15.071x: The Analytics Edge

School: MITx
Course Code: 15.071x
Classes Start: 4 Mar 2014
Course Length: 11 weeks
Estimated effort: 8 to 10 hours/week

Prerequisites:

Basic mathematical knowledge (at a high school level). You should be familiar with concepts like mean, standard deviation, and scatterplots.

15.071x | The Analytics Edge | Verified

The Analytics Edge

Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.

About this Course

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. 

Ways to take this edX course:

Simply Audit this Course

Audit this course for free and have complete access to all of the course material, tests, and the online discussion forum. You decide what and how much you want to do.

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Pursue a Verified Certificate of Achievement

Plan to use your completed coursework for job applications, promotions or school applications? Then you may prefer to work towards a verified Certificate of Achievement to document your accomplishment.

Course Staff

  • Dimitris Bertsimas

    Dimitris Bertsimas

    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

    Allison O'Hair

    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

    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

    Iain Dunning

    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 King

    Angie King

    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 is a TA for 15.071 at MIT this spring.

  • Velibor Misic

    Velibor Misic

    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 last year and will be a TA for the residential version of 15.071 in Spring 2014.

  • Nataly Youssef

    Nataly Youssef

    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.

Prerequisites

Basic mathematical knowledge (at a high school level). You should be familiar with concepts like mean, standard deviation, and scatterplots.

FAQs

What is the format of the class?

The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a “quick question” to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we’ll use in the course. See the Software FAQ below for more info). In the middle of the class, we will run an analytics competition, and at the end of the class there will be a final exam, which will be similar to the homework assignments.

How can I learn more about the course content?

Please watch the video on this page to learn more about the motivation and content of this course.

Will the text of the lectures be available?

Yes, transcripts of the course will be made available.

Do I need to watch the lectures live?

No. You can watch the lectures at your leisure.

How much does it cost to take the course?

Nothing; the course is free. However, if you would like an ID verified certificate, there is a fee. More information on ID Verified Certificates.

Is there a required textbook?

No. All of the material needed is included in the course and is viewable online.

Will I earn a certificate?

This course awards certificates to anyone who registers for a Certificate of Achievement and completes the requirements to pass the course. If you can't pay for a ID-Verified certificate or don't have the required equipment for verifying your identify, you can opt for an Honor Code certificate when you choose your student track (see Certificate of Achievement).

What do I need to know about the topic prior to enrolling in the course?

You only need to know basic mathematics. For most people, this is equivalent to basic high school mathematics. You should know concepts like mean, standard deviation, and histograms.

If I have prior experience with this subject, will the course be useful?

Yes! In each lecture, recitation, and homework assignment, we use a different dataset and case to illustrate the method. Even if you are familiar with all of the methods taught, you can still learn a lot from the different examples.

What software do we use in this class?

We’ll be using two software programs in this class: R and LibreOffice. Both are free online, and you don’t need to be familiar with either of them to take the course. R is a free statistical and computing software environment and LibreOffice is similar to MS Office but a free open source program. Specifically we’ll use the LibreOffice module, Calc in this course. Don’t worry though - we’ll teach everything from scratch!

Other questions

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