# The Analytics Edge

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.

## Get a Reminder

Rating | 4.7★ based on 123 ratings |
---|---|

Length | 13 weeks |

Effort | 10 - 15 hours per week |

Starts | Sep 14 (19 weeks ago) |

Cost | $199 |

From | Massachusetts Institute of Technology, MITx via edX |

Instructors | Dimitris Bertsimas, Allison O'Hair, John Silberholz, Iain Dunning, Angie King, Velibor Misic, Nataly Youssef, Alex Weinstein, Jerry Kung |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Data Science |

Tags | Data Analysis & Statistics |

## Get a Reminder

## Similar Courses

## What people are saying

**
machine learning
**

This would give the right blend of R programming as well as the concepts of data science & machine learning.

It's one of the best courses for introduction to data science including machine learning.

Lots of interesting subjects (regression, machine learning, optimization, ...) with a practical hands-on approach.

Every discussed concepts of machine learning field was accompanied with real world problems and solutions.

Some other MOOCs had introduced even more challenging concepts (Stanford's excellent Introduction to Statistical Learning and Andrew Ng's Machine Learning course on Coursera).

This is one of the best courses on the topic of Analytics/Data Science/Machine Learning that is out there.

The course didn't disappoint, providing plenty of insight into theory and especially applications of machine learning and analytics.

I would recommend taking Coursera's Machine Learning if you'd like to learn more about underlying theory - these two courses make a great pairing.

Read more

**
data science
**

You'll get understanding of some most famous problems in data science (IBM Watson etc.)

I'd definitely recommend this course to anyone who is interested in pursuing career in data science.

One of the best and more thorough courses on data science.

I was thinking that it would be difficult for me to learn data science using R. But, it proved to be amazing.

Very useful class for data analysts.You learn a lot of cool R tricks and basic concepts,but if you are a student and not engaging in a data science project,it's very easy to forget everything you have learned.

If you are interested in having a first contact with R and to learn about some of the capabilities of data science, this course give a really good first idea.

If you only want to take one course to learn data science using R, take this one.

Covers key areas in data science, and opens door to future exploration.

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**
kaggle competition
**

I did the Kaggle competition and finished in the middle of the pack (in the lower 0.6xx accuracy).

7 weeks into the class you take the training wheels off by participating in a Kaggle competition (open only to the class) where you apply the tools you have learned to predict happiness based on demographic and survey data from an app.

Is very well designed, also the most fun part was the Kaggle competition where you have to put all your knowledge that you have learned in the course.

Relevant Kaggle competition.

You'll feel empowered after this course as you'll have learned quite a bit of R. I would pair this course with the first courses of Coursera's Johns Hopkins Data Science Specialization, especially R Programming, to make the Kaggle competition more manageable.

The required kaggle competition was a fun interlude, and might well have served as a gateway to a future addiction.

The best part of this course is by far the Kaggle competition.

The Kaggle competition provided by the instructors is well organized and help you to gain deep understanding of practical use cases.

Highly hands-on: you can work through all the problems following the guidance, and have experience participating in Kaggle competition.

As a consequence, results for the auto-graded Kaggle competition weren't released for two weeks and, when they came out, there was no explanation about how grades were computed, how the problem should have been approached (our independent variable was not well chosen, given the limitations of the data set), or why some students had weirdly high scores (they cheated, it turned out).

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**
very well
**

It is very well-structured with exercises and excellent explanation along the whole course.

The objective are very well developed and explained in a way that is very comfortable to follow.

This course goes very well with Intro Statistical Learning from Stanford Online.

The homework problems for this course are very well crafted and look at a variety of interesting data sets from basketball stats to tweets about Apple.

It is very practical, very well designed, and have many applications drilled into us so that we remember.

Lot of practice exams, the concepts are drilled very well.

Very practicle and very well designed.

The way he talks through videos is just halting and uninteresting - please let the girls speak, they do it very well!

Read more

**
case studies
**

There are several case studies discussed as part of the course which give a good understanding of data analysis and application of various algorithms.

Lectures are detailed yet concise, focused on interesting case studies, combined with plenty of practical exercises to make sure you have understood the material presented.

Excellent course, well structured and with very interesting case studies.

The case studies were quite unique and exhaustive.

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**
final exam
**

One week is devoted to an analytics competition while the final week is reserved for a 4 part final exam.

It is challenging and just the challenge would give me sense of fulfillment when submitting the final exam.

The final exam was very well pitched and required using most of the techniques we learned during the course.

Graded course assignments include "quick questions" that accompany the lectures, a pair of problem sets for each unit, a comprehensive final exam, and a Kaggle competition that pits you against your classmates in making predictions from a data set using all you have learned during the first half of the course.

The general lack of staff communicativeness also led to grumbling about the final exam, with numerous discussion posts questioning this or that problem.

Read more

**
analytics edge
**

MIT’s The Analytics Edge is an edX course focused on using statistical tools to gain insight about data and make predictions.

Have completed a number of MOOCs, and I put the Analytics Edge up there as one of my favorites, along with Andrew Ng's Machine Learning class and Rice's Python programming class.

The Analytics Edge is a meaty course.

The Analytics edge is an excellent course that teaches a bunch of practical statistical tools and actually gives you enough practice using them through the lengthy homework exercises to gain some confidence with them and remember how to use them.

Taking Analytics Edge, I now have a better understanding on how to tackle problems involving data, and how to apply analytics in different fields.

The Analytics Edge (TAE) provides a solid, engaging introduction to the techniques of "big" data analysis and machine learning -- and much more.

I've completed Spring-Summer 2016 session of The Analytics Edge.

Read more

**
real world
**

The course uses real world examples of how analytics have been used to gain a competitive edge.

Excellent introduction of real world analytics using R I have taken several online statistics courses and a couple basic courses on using R software.

Thanks to all the instructors & support staff for a great course This courses use real world examples to teach several basic but important statistical models, in order to gain analytical edge.

Read more

**
problem sets
**

Endless problem sets - many of them based on real data - will definitely help you in this.

While it does do this, the greater focus (and value) is in that it gets your hands dirty applying the most essential of analytical techniques to gain insight from a number of datasets using R. The weekly structure was two lectures sequences demonstrating that week's analytical technique on two different datasets, a recitation applying these methods on a third set, and then problem sets, typically applying what you learned in the lectures on 4 different datasets.

The exercises and problem sets are based on real-world data, even if simplified, it was still very interesting to solve real problems with knowledge obtained from lecture.

The problem sets, while interesting, progressive and well thought out, can be tedious -- a lot of the tasks are trivial and repetitive, and it's not always clear what lesson the authors are driving at.

Read more

## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

BUSINESS ANALYTICS SPECIALIST - ENTERPRISE ANALYTICS $53k

Analytics/F&B Management $77k

Global Analytics $89k

V.P. Analytics $89k

Analytics Scientist $89k

Business Analytics & Advanced Analytics $90k

Digital Analytics $93k

Analyst, Analytics $94k

BI & Analytics $106k

Product Analytics $116k

Mobile Analytics $116k

Senior Analytics $198k

## Write a review

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Rating | 4.7★ based on 123 ratings |
---|---|

Length | 13 weeks |

Effort | 10 - 15 hours per week |

Starts | Sep 14 (19 weeks ago) |

Cost | $199 |

From | Massachusetts Institute of Technology, MITx via edX |

Instructors | Dimitris Bertsimas, Allison O'Hair, John Silberholz, Iain Dunning, Angie King, Velibor Misic, Nataly Youssef, Alex Weinstein, Jerry Kung |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Data Science |

Tags | Data Analysis & Statistics |

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