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  • Provider edX
  • Subject Data Science
  • $ Cost Free Online Course
  • Session Self Paced
  • Language English
  • Effort 10-15 hours a week
  • Start Date
  • Duration 12 weeks long

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Overview

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). In the middle of the class, we will run an analytics competition, and at the end of the class there will be a final exam, which will be similar to the homework assignments.

Taught by

Dimitris Bertsimas

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Reviews for edX's The Analytics Edge
4.7 Based on 77 reviews

  • 5 stars 71%
  • 4 stars 23%
  • 3 stars 5%
  • 2 star 0%
  • 1 star 0%

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  • 1
Life S
5.0 4 years ago
Life completed this course.
MIT’s The Analytics Edge is an edX course focused on using statistical tools to gain insight about data and make predictions. The majority of the course teaches analytic methods using the R programming language, but the final 2 weeks deal with solving optimization problems using spreadsheet software (LibreOffice or MS Excel). The course runs 11 weeks and covers R basics, linear regression, logistic regression, decision trees, text analytics, clustering, visualizations and both linear and integer optimizations.

The Analytics Edge is a meaty course. It has a lot of content each week…
28 people found
this review helpful
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Erwin E
3.0 9 months ago
Erwin audited this course.
As a software engineer interested in ML techniques and algorithms, I did not enjoy this course. I had previously completed the popular coursera ML course by Andrew Ng which I enjoyed, and I was hoping in this course to both get familiar with R as well as get my hands dirty with real-world scenarios.

Unfortunately, this course was a painful approach to learning R and analytics, and I can't help but feel that it could've been done much better. My complaints:

- R: Not much time spent familiarizing with the basics of R, and instead being thrown into the world of R analyti…
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Ilya R
5.0 3 years ago
by Ilya completed this course, spending 10 hours a week on it and found the course difficulty to be hard.
If you're like me prefer study by doing this course is for you. Endless problem sets - many of them based on real data - will definitely help you in this. You'll get understanding of some most famous problems in data science (IBM Watson etc.) - just watch the first lecture to get an overview of them.

Probably the best part of the course is Kaggle competition - you'll be able to understand the gap between guided problem sets and real-life situations. Don't be discouraged if you can' get in TOP from your first attempt. It's not that easy.

This course is not about math. If you're interested in some math background go to Stanford course on statistical learning.
10 people found
this review helpful
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Robert V
5.0 2 years ago
by Robert audited this course, spending 12 hours a week on it and found the course difficulty to be medium.
I didn't take this course for credit or certificate because I already have a MS in EE and an MBA, and I was taking other classes simultaneously. My goal was to "skim" the content for expansion in the future. However, the content and exercises were so well organized (most step-by-step) and relevant to real-world problems that I ended up spending lots of time understanding the material and writing lots of R code that I archived for future reference. The course wasn't terribly difficult, but there was a lot of material. I skipped the optional lessons until I completed the course, but now I am go…
1 person found
this review helpful
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Robert R
5.0 3 years ago
by Robert partially completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
Note: There was not a session currently ongoing so I just watched the videos and completed most of the assignments.

This is a good course if you are looking to either learn some easy data analysis with R or the basics of different analytical tools. If you already have even a little programming background, you can probably coast through this course pretty easy but the knowledge is still worthwhile (I was able to complete what I wanted in about a week).

I am more of a practical learner so the real-world examples were infinitely useful in aiding understanding. The R walkthroughs were also well done and already helped me apply those concepts to my own independent analysis of other data sets.
1 person found
this review helpful
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Anonymous
5.0 4 years ago
Anonymous completed this course.
This course that has given me a working understanding of R and the core statistical modeling techniques that you would find, for example, in James et al, "An Introduction to Statistical Learning". It is a very problem-oriented, hands-on course with a nontrivial workload, but in my experience so far, it has been very effective. The homework problems are very practical and illustrate the underlying statistical concepts very nicely in real-world settings.
4 people found
this review helpful
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Ronny W
5.0 3 years ago
by Ronny completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
One of the best MOOCs I ever followed (up to now completed more than 30).

Good combination of conceptional introduction and on hands experiments.

Lots of fascinating cases worked out with R.

Takes quite some effort to do all the lectures but it is very well worth it.

A must follow for anyone who wants to become a data scientist.
2 people found
this review helpful
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Anonymous
4.0 3 years ago
Anonymous is taking this course right now.
The clarity of exposition in the videos is first class. The breadth of real-world applications is stunning. I do think the time required to complete the homeworks has been severely underestimated. One can spend a good half-hour writing code to get two - yes, two ! - marks out of, say 77. That's crazy.
2 people found
this review helpful
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Anonymous
5.0 3 years ago
Anonymous completed this course.
So far I have completed several online courses and this is by far the best I have come across. It has inspired me to want to learn more about analytics. The course uses real world examples of how analytics have been used to gain a competitive edge. Examples range from election forecasting to discovering patterns for disease detection.
1 person found
this review helpful
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Anonymous
4.0 4 years ago
Anonymous completed this course.
To me it's just time consuming. Every week it's 4 sets of assignments 20+ questions each. On some questions there is only one attempt. But I admit it is a VERY GOOD course for beginners.

Modeling (i.e linear regression, logistic regression etc.) are well explained by examples using R.
2 people found
this review helpful
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Aswitala A
4.0 4 years ago
Aswitala is taking this course right now, spending 8 hours a week on it and found the course difficulty to be easy.
The best thing about that course was competition which provide us with real problem to solve using analytics. It was through Kaggle platform. Another good thing about that course was quite reasonable amount of statistical programming, however there was rather basic concepts.
2 people found
this review helpful
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Anonymous
5.0 3 years ago
Anonymous completed this course.
For the last two years, I have had at least two MOOCS each months. I love learning, and this course is one the best I have had the chance to stumble upon.

The content is extremely interesting, and the way it is organized makes it extremely easy to understand and follow.

1 person found
this review helpful
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Nim J
5.0 4 years ago
by Nim completed this course.
This is one of the best online course available currently. This would give the right blend of R programming as well as the concepts of data science & machine learning. I'd definitely recommend this course to anyone who is interested in pursuing career in data science.
1 person found
this review helpful
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Joalbert P
5.0 a year ago
by Joalbert completed this course, spending 9 hours a week on it and found the course difficulty to be medium.
Excellent course!! 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. For me, it is one of the best course I have ever taken.
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Anonymous
4.0 4 years ago
Anonymous completed this course.
This class is challenging for me, but I have no previous experience with R and very limited experience with statistics. The teaching team does a good job of explaining the material and choosing interesting topics for each section.
1 person found
this review helpful
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Anonymous
5.0 3 weeks ago
Anonymous audited this course.
Quite demanding technically but the chosen examples were fascinating in themselves. As a result gaining a degree of technical competence with analytical data tools but also a variety of context how & where these new skills can be applied.
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Swap S
4.0 4 years ago
Swap is taking this course right now, spending 6 hours a week on it and found the course difficulty to be hard.
Very good pragmatic real learning experience, recommended for all the business analyst and students of statistics. Helps to learn the advance concepts of R very easily. Its best course from the MIT leaders.
1 person found
this review helpful
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Sravya M
5.0 2 years ago
by Sravya completed this course.
One of the best Data science courses, you get to know tons of things with this course, great way to learn R and participate in Kaggle Data Science competition.
2 people found
this review helpful
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Thong T
5.0 3 years ago
by Thong completed this course, spending 15 hours a week on it and found the course difficulty to be very hard.
This is one of the toughest and most enjoyable courses I have ever taken. You are expected to spend at least 10 hours a week to learn all the materials in this course.
1 person found
this review helpful
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Anonymous
5.0 3 years ago
Anonymous completed this course.
One of the best and more thorough courses on data science. Covers the main topics of science data, and homeworks are quite didactical and almost real.
1 person found
this review helpful
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