Course Features

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Duration

13 weeks

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Effort

15 hours per week

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Teaching Type

Instructor Paced

Course Description

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.

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). At the end of the class there will be a final exam, which will be similar to the homework assignments.

Course Overview

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Live Class

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Human Interaction

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Personlized Teaching

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Mathematical maturity and prior experience with programming will decrease the estimated effort required for the class, but are not necessary to succeed.

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

What You Will Learn

An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization

An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software

How to implement all of these methods in R

Course Instructors

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Allison O'Hair

Lecturer at Stanford University

Allison O’Hair is currently a Lecturer in Management at the Stanford Graduate School of Business. Prior to this, she was a lecturer of Operations Research and Statistics at the MIT Sloan School of Ma...
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Angie King

Data Scientist at End-to-End Analytics

Angie King is currently a data scientist at End-to-End Analytics in Silicon Valley, San Francisco. She received her PhD in analytics from the MIT Operations Research Center. Her research investigated...
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Dimitris Bertsimas

Boeing Professor of Operations Research at MIT

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 Resea...
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Iain Dunning

Senior Research Engineeer at DeepMind Technologies Ltd

Iain Dunning is a Senior Research Engineeer at DeepMind Technologies. While at MIT, Iain worked in the MIT Operations Research Center. His research focuses on software and algorithms for optimization...
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