Link to Content:

edX

Created/Published/Taught by:

Massachusetts Institute of Technology

Dimitris Bertsimas

Allison O'Hair

John Silberholz

Iain Dunning

Angie King

Velibor Misic

Nataly Youssef

Alex Weinstein

Jerry Kung

Content Found Via:

edX

Free? Yes

Tags: analytics / CART / clustering / data visualization / linear regression / logistic regression

Difficulty Rating:

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.

**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

Recommended 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.

Go to Content: The Analytics Edge

## By Shery January 7, 2016 - 1:57 am

Analytics Edge is one of the best courses for beginners to get the feel of data science.This is the best designed course that I’ve ever seen.One can learn various techniques like linear regression. logistic regression, CART,Random forests, text analytics etc. Everything is taught in R. So it is understandable for someone who has no programming experience also.

Early Beginner- new to data science / totally new to this topic / few prerequisitesEarly Beginner - was totally new to this topic / had few prerequisites

(3) A Good Amount

No ratings yet.

## By Stephanie Floyd April 22, 2017 - 3:48 pm

The course taught a variety of prediction and classification methods through real world problems/datasets in a way that is accessible to even early beginners. The course also teaches R as it progresses, so a background in R is not completely necessary – though so familiarity with R is helpful. The course finishes with a Kaggle competition that helps students use what they have learned in a comprehensive way. I would recommend this course to anyone with little to no experience with data science as well as anyone looking for a refresher course basic in machine learning and R

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesAdvanced Beginner - was new to this topic / had some prerequisites

(4) A Lot

Detailed Content Survey:Needed to gain or strengthen general data science knowledge

This was my first experience with this publisher/series

Yes

Most (i.e. read the entire book but didn't complete the exercises)

A basic familiarity with R is helpful, but not imperative.

Internet, R, R Studio

I gained a basic understanding of how various machine learning methods can be applied as well as how to apply them through R

I used Evernote to take notes during the video lectures, because I found that sometimes during a programming exercise I'd need to go back and rewatch a video to solve a problem. I'd recommend any note-taking, but found Evernote to be helpful because I could copy and paste code snippets into the notes as well.

I tried to stick to a unit per week. It was about 3-5 hours of work, but felt like a reasonable pace

The course is about 9 units and I spent about 3-5 hours per unit