Name: The Analytics Edge [edX] Summer 2015
Creator: None
License: https://academictorrents.com/nolicensespecified

The Analytics Edge [edX] Summer 2015 (319 files)

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

Bibtex:

@article{,
title= {The Analytics Edge [edX] Summer 2015},
journal= {},
author= {},
year= {},
url= {https://www.edx.org/course/the-analytics-edge-0},
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

Prerequisites
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.


https://www.edx.org/course/the-analytics-edge-0},
keywords= {Machine Learning, Analytics, MIT, R},
terms= {},
license= {},
superseded= {}
}