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edX: The Analytics Edge

 with  Dimitris Bertsimas
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#2 in Subjects > Data Science

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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.

76 Student
reviews
Cost Free Online Course
Pace Self Paced
Subject Data Science
Provider edX
Language English
Hours 10-15 hours a week
Calendar 12 weeks long

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In-Depth Review
Difficult but fulfilling course that will provide you with endless problem sets – many of them based on real data. The Analytics Edge is best suited for business students. Read Review
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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
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How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

Reviews for edX's The Analytics Edge
4.7 Based on 76 reviews

  • 5 stars 71%
  • 4 stars 24%
  • 3 stars 5%
  • 2 star 0%
  • 1 star 0%

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  • 1
5.0 4 years ago
Life is Study 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 and it’s not easy to breeze through things like it is with many other MOOCs. There are graded quizzes after each video lecture and each week of new material has 4 fairly lengthy case studies to complete. One week is devoted to an analytics competition while the final week is reserved for a 4 part final exam. Some students on the forums claimed they were spending 10 to 15 hours a week on this cour
Read more
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 and it’s not easy to breeze through things like it is with many other MOOCs. There are graded quizzes after each video lecture and each week of new material has 4 fairly lengthy case studies to complete. One week is devoted to an analytics competition while the final week is reserved for a 4 part final exam. Some students on the forums claimed they were spending 10 to 15 hours a week on this course. Coming into the course with basic knowledge of statistics and R helps a lot. It should be noted, however, that this course is not too math intensive. It doesn't spend a lot of time talking about formulas or nitty-gritty mathematical details; it mostly teaches you how to apply statistical functions and methods and interpret the results.
25 people found
this review helpful
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3.0 4 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 analytic libraries, each library having its own arbitrary syntax and function parameters. I ended up really disliking R.

- A very poor way of teaching how to play with data. The student isn't challenged to "figure out what to do next with this data" and is instead spoon-fed the next step, having only to reproduce the correct library/function/syntax from memory. Learning by repetition, not by
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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 analytic libraries, each library having its own arbitrary syntax and function parameters. I ended up really disliking R.

- A very poor way of teaching how to play with data. The student isn't challenged to "figure out what to do next with this data" and is instead spoon-fed the next step, having only to reproduce the correct library/function/syntax from memory. Learning by repetition, not by deeper understanding.

- The step-by-step assignments are also designed such that you have enter the results of each step one at a time, in order to verify that you're getting the right results, scoring micro-points along the way. Time-consuming and cannot be sped up. The weekly lecture videos can be watched in 1-2 hours, but the assignments/recitations take 8+ hours.

- A very poor presentation of the theory behind the techniques. Slides are presented and mostly simply read off of. I'm sorry but this course needs to find better teachers. A couple of the recitation TAs were pretty good though and I appreciated them more.

On the positive side, what I got out of the course:

- Some hands-on experience with real-world data analysis. Reality-check that most applications of data analysis don't involve chinese boardgames and self-driving cars.

- Some familiarity with R, contrasting it with Octave/Matlab, and also realizing that there are libraries for everything.
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5.0 3 years ago
by Ilya Rudyak 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.
8 people found
this review helpful
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5.0 2 years ago
by Robert Vangen 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 going back and doing the optional lessons. I did the Kaggle competition and finished in the middle of the pack (in the lower 0.6xx accuracy). To get in the "leader" category (two in the 0.9xx accuracy) will require a lot of work and knowledge of R beyond what is covered by the course. If you want to be among the leaders, be prepared to do a lot of Web searches and searching R documentation. I learne
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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 going back and doing the optional lessons. I did the Kaggle competition and finished in the middle of the pack (in the lower 0.6xx accuracy). To get in the "leader" category (two in the 0.9xx accuracy) will require a lot of work and knowledge of R beyond what is covered by the course. If you want to be among the leaders, be prepared to do a lot of Web searches and searching R documentation. I learned a lot, but I'm still very humble and respectful of the experts.
1 person found
this review helpful
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5.0 2 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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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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5.0 3 years ago
by Ronny De Winter 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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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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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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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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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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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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5.0 3 years ago
by Nim J 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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5.0 10 months ago
by Joalbert Palacios 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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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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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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5.0 2 years ago
by Sravya Madipalli 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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5.0 2 years ago
by Thong Buu Tran 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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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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4.0 2 years ago
Anonymous completed this course.
You might think twice about this course if all you have had is HS algebra and a smattering of statistics. I had a stats course (admittedly 40 years ago), an MS is Computer Science (also 40 years ago) and 35 years in IT and found this course challenging.

The following statement from MIT.Edx should be taken with a grain of salt.

You only need to know basic mathematics. For most people, this is equivalent to basic high school mathematics. You should know concepts like mean, standard deviation, and histograms. This course is also useful for those who already have experience in the subject. In each lecture, recitation, and homework assignment, we use a different dataset and case to illustrate the method. Even if you are familiar with all of the methods taught, you can still learn a lot from the different examples.

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