The Analytics Edge [edX] Summer 2015

folder The Analytics Edge [edX] Summer 2015 (319 files)
file2 Linear Regression/2_10.mp4 124.20MB
file6 Clustering/6_1.mp4 4.89MB
file6 Clustering/6_22.mp4 8.47MB
file6 Clustering/6_11.mp4 14.41MB
file6 Clustering/6_19.mp4 12.76MB
file6 Clustering/6_6.mp4 7.51MB
file6 Clustering/6_3.mp4 11.42MB
file6 Clustering/6_13.mp4 9.14MB
file6 Clustering/6_14.mp4 3.30MB
file6 Clustering/6_20.mp4 17.35MB
file6 Clustering/6_17.mp4 27.29MB
file6 Clustering/6_15.mp4 3.28MB
file6 Clustering/6_7.mp4 38.15MB
file6 Clustering/6_16.mp4 8.45MB
file6 Clustering/6_12.mp4 12.56MB
file6 Clustering/6_5.mp4 13.22MB
file6 Clustering/6_10.mp4 6.22MB
file6 Clustering/6_21.mp4 32.58MB
file6 Clustering/6_8.mp4 30.15MB
file6 Clustering/6_18.mp4 29.66MB
file6 Clustering/6_4.mp4 7.94MB
file6 Clustering/6_9.mp4 8.22MB
file3 Logistic Regression/3_5.mp4 43.44MB
file3 Logistic Regression/3_8.mp4 19.54MB
file3 Logistic Regression/3_13.mp4 6.47MB
file3 Logistic Regression/3_16.mp4 2.89MB
file3 Logistic Regression/3_10.mp4 10.98MB
file3 Logistic Regression/3_4.mp4 9.03MB
file3 Logistic Regression/3_15.mp4 10.89MB
file3 Logistic Regression/3_11.mp4 9.68MB
file3 Logistic Regression/3_2.mp4 8.83MB
file3 Logistic Regression/3_18.mp4 23.56MB
file3 Logistic Regression/3_1.mp4 4.69MB
file3 Logistic Regression/3_9.mp4 6.55MB
file3 Logistic Regression/3_12.mp4 33.19MB
file3 Logistic Regression/3_17.mp4 11.32MB
file3 Logistic Regression/3_7.mp4 21.93MB
file3 Logistic Regression/3_3.mp4 8.44MB
file3 Logistic Regression/3_19.mp4 19.00MB
file3 Logistic Regression/3_6.mp4 21.00MB
file3 Logistic Regression/3_14.mp4 4.37MB
file3 Logistic Regression/3_21.mp4 12.54MB
file3 Logistic Regression/3_20.mp4 27.93MB
file8 Linear Optimization/8_22.mp4 43.29MB
file8 Linear Optimization/8_20.mp4 11.17MB
file8 Linear Optimization/8_18.mp4 11.81MB
file8 Linear Optimization/8_12.mp4 34.85MB
file8 Linear Optimization/8_4.mp4 8.45MB
file8 Linear Optimization/8_1.mp4 4.25MB
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Type: Course
Tags: machine learning, analytics, MIT, R

title= {The Analytics Edge [edX] Summer 2015},
journal= {},
author= {},
year= {},
url= {},
abstract= {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 Skip 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.

What you'll learn
An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization
How to implement all of these methods in R
An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software

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.},
keywords= {Machine Learning, Analytics, MIT, R},
terms= {},
license= {},
superseded= {}

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