The Analytics Edge

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10/10 stars
based on  149 reviews
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edX online courses
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Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

Provider Subject Specialization
Sciences & Technology
Business & Management
16618 reviews

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 ena...

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.

Reviews 10/10 stars
149 Reviews for The Analytics Edge

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Student

6/10 starsCompleted
3 years, 2 months ago
I agree with all the other reviews regarding the course material: the lectures are great and cover fantastic concrete applications of data analytics in business. The Kaggle competition was another brilliant idea perfectly executed. However, despite these strengths, the course experience is severly damaged by the length of the weekly problems. I sometimes have the feeling I am the only one thinking this way, but I'll share my opinion anyway. The problems contain an insane amount of trivial questions that usually require nothing more than copying/pasting a formula available in the source code provided with the lectures. Don't get me wrong: I am fine investing a significant amount of time in a MOOC. My point here is that the time spent on the problems brought no added value. The questions offer no challenge: the datasets are always perfectly clean and formatted. The questions are like "Type summary(dataset). What value can you read for ... I agree with all the other reviews regarding the course material: the lectures are great and cover fantastic concrete applications of data analytics in business. The Kaggle competition was another brilliant idea perfectly executed. However, despite these strengths, the course experience is severly damaged by the length of the weekly problems. I sometimes have the feeling I am the only one thinking this way, but I'll share my opinion anyway. The problems contain an insane amount of trivial questions that usually require nothing more than copying/pasting a formula available in the source code provided with the lectures. Don't get me wrong: I am fine investing a significant amount of time in a MOOC. My point here is that the time spent on the problems brought no added value. The questions offer no challenge: the datasets are always perfectly clean and formatted. The questions are like "Type summary(dataset). What value can you read for field ... ?" The way the problems were framed gave the impression that data science is a clean, linear, deterministic process that offers no challenge, no suprise and no headache. The problems were not leveraged to introduce basic R concepts such as FOR loops or IF conditions. They were not used either to show how data sets often come sparse, heterogeneous, dirty, hard to decipher not to say "hostile". Key concepts such as overfitting and cross validation were barely mentionned, while they should have been exposed through specific dedicated problems. With so much of real data science tasks and concepts left aside, many students felt helpless when the Kaggle competition started. Many had no clue what to do with missing values, what to do with "Yes/No" values: convert to 0/1 ? -1/1 ? Should we scale ? What should we do with obviously wrong data (Year of brith in the future) ? There are more than 100 features available. Is that too many ? How do I know if that's too many ? I want to try a model with fewer features. How do I select the "best" ones ? My model performs great on the training set but not on the test set. What happens ? How do I fix that ? Etc. Unsurprisingly, so many hours spent answering trivial closed questions and no methodolical open questions produced very little actionable skills and experience when the time for Kaggle came. It may sound hard, but I am optimistic: the problems can be rebalanced pretty easily for the next iteration of the course, should the staff agree with the diagnostic. As so many others have said: the rest of the material is fantastic.
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Akeel Ahamed profile image
Akeel Ahamed profile image

Akeel Ahamed

10/10 starsTaking Now
3 weeks, 1 day ago
An amazing course for presented by amazing instructors from an amazing University! I thoroughly enjoyed the real world examples being applied to the analytic theory presented, in the fields of sports(especially my favourite sport of basketball) , quality of Healthcare, the optional assignments tackling the car industry and so much more. This is wonderful! I would love to take this course again soon!
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Anirban Ghatak profile image
Anirban Ghatak profile image

Anirban Ghatak

10/10 starsCompleted
1 month, 2 weeks ago
This is THE COURSE that made me a fan of Data analytics. Outstanding content, presentation and the quizzes. This will definitely take more time then you anticipate as the course gets pretty intense after first 4 weeks. I took it last year and will do it again this year.
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Kareem ali profile image
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Kareem ali

10/10 starsTaking Now
1 month, 3 weeks ago
This is by far the best course in all data analtyicts, the knowledge of the teachers is incredible and is one of the best out there.
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Addy Mell profile image
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Addy Mell

10/10 starsCompleted
2 months, 2 weeks ago
This is one of the very best courses I've taken in my entire life. I really don't think the videos can be better and I certainly won't have passed the course without them. The pedagogy was also great.
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student

10/10 starsCompleted
2 months, 3 weeks ago
Great course. The lectures and homework are well organized. Highly recommended if you are willing to put in the time and effort.
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student

10/10 starsCompleted
3 months ago
I can confidently say that this is one of the best courses that I have taken online. There is a good mix of theory, practice and repetition in the course so that the concepts taught in class is really understood. Thanks to the instructors, MIT and the people who worked behind the scenes for making such a wonderful course available for everybody.
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Kishore Kolla profile image
Kishore Kolla profile image

Kishore Kolla

10/10 starsDropped
4 months, 1 week ago
Its just excellent and it is all about real time scenarios. never comparable this course with any of the other. waiting for one more time to start this course.
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Payal Agrawal profile image
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Payal Agrawal

10/10 starsCompleted
4 months, 2 weeks ago
the best course I took. very professional, very good learning and it challenged me a lot. very structurally designed and I didn't need any prior experience to do this course. I did all online classes, gave all exams (which weren't easy at all) and did good hands on practice while doing the course, IT IS REALLY PER MIT STANDARDS! LOVED IT!
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v m kishore Thadiboyina profile image
v m kishore Thadiboyina profile image

v m kishore Thadiboyina

9/10 starsCompleted
5 months, 2 weeks ago
an excellent course well designed with a practical implementation . thanks to EDX. This course really helps some one who want to progress further beyond the basics of machine learning . The model evaluation OCR is well explained.
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Dmitry Kozhedubov profile image
Dmitry Kozhedubov profile image
10/10 starsCompleted
  • 23 reviews
  • 15 completed
5 months, 2 weeks ago
I've completed Spring-Summer 2016 session of The Analytics Edge. I wanted to take this course for a very long time, so enrolled as soon as edX announced a new session. As an additional motivation to complete the course, I've purchased a verified certificate. The course didn't disappoint, providing plenty of insight into theory and especially applications of machine learning and analytics. I like to go back to course materials in my work, specifically when working on classification and linear optimization problems. 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. What could be better: while sufficient introduction to R is provided, more complex portions of code are not explained, so you'll spend significant time browsing R docs if you want more than just memorize commands or copy and paste. Also, most of the homework questions don't... I've completed Spring-Summer 2016 session of The Analytics Edge. I wanted to take this course for a very long time, so enrolled as soon as edX announced a new session. As an additional motivation to complete the course, I've purchased a verified certificate. The course didn't disappoint, providing plenty of insight into theory and especially applications of machine learning and analytics. I like to go back to course materials in my work, specifically when working on classification and linear optimization problems. 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. What could be better: while sufficient introduction to R is provided, more complex portions of code are not explained, so you'll spend significant time browsing R docs if you want more than just memorize commands or copy and paste. Also, most of the homework questions don't leave much room for imagination and are pretty straightforward.
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student

10/10 starsCompleted
5 months, 3 weeks ago
One of the best classes in MOOC and I have taken a lot of them over the last couple of years. A really gifted teacher
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Saurabh Joshi profile image
Saurabh Joshi profile image

Saurabh Joshi

10/10 starsTaking Now
5 months, 3 weeks ago
Best Best and The Best.. i admire the whole team involved in making analytics learning so easy. Everything at just one place is really brilliant. I will definetely learn and get success in this career in my life with all your support thank you Team MIT. :)
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David C profile image
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David C

9/10 starsCompleted
8 months, 1 week ago
In general, the course was really nice and I have learnt a lot. However, it would have been nice if some programming concepts, such as loops, had been introduced.
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Student

10/10 starsCompleted
8 months, 2 weeks ago
Recently completed my 50th MOOC! The Analytics Edge from MIT was my first one several years ago, and is still one of my top three favorites; the other two are Machine Learning from Stanford and Learning from Data from Caltech. The Analytics Edge is a quality course that is ideal for a first course in the data science and machine learning fields. Lecture videos and topic explanations are very clear, enabling one to learn quickly. It was very apparent right from the outset that the course development team had put forth an amazing effort in preparing this course, and the high level of quality persisted throughout the course. There are about 6-8 weekly problem sets. One could easily skip a few of them each week and still learn the material well, but if you're planning to do them all and are new to the field, plan on 10-15 hrs of effort each week.
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Brandon Howell profile image
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Brandon Howell

10/10 starsCompleted
8 months, 2 weeks ago
One of the best courses hands down on edx. This course has very engaging lecture material, does a very good job at outlining practical application of the topics, and gives a competitive edge to those who utilize the content in their career. Personally speaking, I was able to save my company $75k+ per year through a resource management program I developed using principles from this class. Take this class. Even with no prior experience, it is a very manageable course.
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v m kishore Thadiboyina profile image
v m kishore Thadiboyina profile image

v m kishore Thadiboyina

10/10 starsCompleted
8 months, 3 weeks ago
This course is simply awesome !!! I love this .Thanks for EDX and Analytics edge Team. All the topics are covered with complete understanding and even a person who is new to Machine learning can get a nice intuition at this course.
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Hasan Azmat profile image
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Hasan Azmat

10/10 starsCompleted
9 months, 2 weeks ago
This is great course very thorough indeed and detailed with teachers who were experts in the field and showed great interest n teaching the materials
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10/10 starsCompleted
  • 2 reviews
  • 1 completed
1 year ago
This was the first MITx course I attempted, and I'm very impressed. It's amazing that a course of this depth and breadth is available for free. So much material is covered here, lots of different types of modeling with R and even units on linear and integer optimization with Excel. The course is well supported by Community TAs who are very involved on the discussion forums. Be warned that to do well in this class you have to be willing to commit many hours per week to completing the homework assignments, as they are very long. But if you put the time and effort in, you will come out with a solid grasp on data analysis and using R. I hope future versions of the class cover more on data wrangling and tidying, but aside from that - awesome course. Obviously lots of work was put into developing it, and, given the number of "Thank you"s that appeared on the discussion forum at the end, you know the students appreciated it.
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Salman Ali profile image
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Salman Ali

10/10 starsCompleted
10 months ago
This is one of the top MOOCs of ever , no question about it it is very hard but everything is explained as well so no excuses of not doing well.
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CHONHUA GUO profile image
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CHONHUA GUO

10/10 starsCompleted
10 months ago
This is a course I would like to come back again for N times! A well designed course which not only really help make learning happen, but also pedagogically make MIT motto "Mens et Menus" real. I have particularly benefitted from two elements of this wonderful course: 1)Videos with carefully designed and well prepared interpretation which clearly illustrates how a specific Machine Learning technique can address a real life problem step by step with R language; 2)Kaggle competition which greatly foster learners to focus on learning by use what they learned from the course; Actually, I have personally engaged in this part for twice and gained a lot from that. I would deeply congratulate and thank the instruction team for their endeavor to make this master piece with strong MIT motto style happen! If kindly permitted for any suggestion for further improvement, maybe a Synchronous Python version also provided during the course would be g... This is a course I would like to come back again for N times! A well designed course which not only really help make learning happen, but also pedagogically make MIT motto "Mens et Menus" real. I have particularly benefitted from two elements of this wonderful course: 1)Videos with carefully designed and well prepared interpretation which clearly illustrates how a specific Machine Learning technique can address a real life problem step by step with R language; 2)Kaggle competition which greatly foster learners to focus on learning by use what they learned from the course; Actually, I have personally engaged in this part for twice and gained a lot from that. I would deeply congratulate and thank the instruction team for their endeavor to make this master piece with strong MIT motto style happen! If kindly permitted for any suggestion for further improvement, maybe a Synchronous Python version also provided during the course would be great!
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V C profile image
V C profile image

V C

10/10 starsCompleted
10 months, 1 week ago
This was such a great course. It might have just changed my career. I am going to do this course again when it comes up.
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Sagar Sant profile image
Sagar Sant profile image

Sagar Sant

10/10 starsCompleted
10 months, 1 week ago
Excellent course to understand topics in analytics, machine learning, artificial intelligence right from basics. Course is designed far better than what you will expect from 'About the course'. It also teaches 'R programming' and you will encounter at least 15 additional R packages used in Machine Learning. These analytics concepts are explained with the help of real world examples which is best part of this course. Both, instructors and teaching staff have put lot of efforts in designing this course. I would express my sincere thanks to Professors and Teaching Staff.
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Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 53 reviews
  • 52 completed
1 year ago
The Analytics Edge (TAE) provides a solid, engaging introduction to the techniques of "big" data analysis and machine learning -- and much more. The "much more" is how it extends the nuts and bolts of data analytics into higher-level questions of strategy, i.e., using tools not only to answer questions, but to gain an edge. The course is built around the clunky but powerful R language and, at the end, the use of spreadsheet functions to perform optimizations. Topics covered include linear and logistic regression, classification and regression trees and forests, clustering, text analytics (word clouds!), clustering, data visualization, and linear and integer optimization. Each unit consists of three lecture sequences that teach implementation of a specific technique in the context of two or three illustrative, and very interesting, problems; and a homework assignment where you apply what you've learned to a new problem. The probl... The Analytics Edge (TAE) provides a solid, engaging introduction to the techniques of "big" data analysis and machine learning -- and much more. The "much more" is how it extends the nuts and bolts of data analytics into higher-level questions of strategy, i.e., using tools not only to answer questions, but to gain an edge. The course is built around the clunky but powerful R language and, at the end, the use of spreadsheet functions to perform optimizations. Topics covered include linear and logistic regression, classification and regression trees and forests, clustering, text analytics (word clouds!), clustering, data visualization, and linear and integer optimization. Each unit consists of three lecture sequences that teach implementation of a specific technique in the context of two or three illustrative, and very interesting, problems; and a homework assignment where you apply what you've learned to a new problem. The problems are engaging and well chosen -- the real deal, applications of data analytics you've heard of and surely wondered about: Moneyball, spam filtering, healthcare outcomes and spending decisions, how businesses such as airlines segment customers to understand their markets. If this were not enough, it's only the foundation. Where the course really shines is in taking the analysis to the next level and addressing questions of strategy. Say you're a parole board member trying to predict whether a violator is likely to commit a new crime. You use various techniques to finesse a model, yet find that its predictive accuracy is no better than a baseline guess. But wait! Although the overall accuracy may be no better, your model may be tuned to virtually eliminate false negatives -- prisoners you may have thought, wrongly, were safe to release, but weren't, at the expense of false positives (prisoners you could have released, but didn't). If the strategic concern is public safety, overall model accuracy is far less important than its performance along the key strategic metric. 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. Although this is, overall, a fabulous course, there are a few negatives. 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. And while the community TAs were dedicated (and often heroic) in fielding questions and offering help, the actual course staff was mostly absent -- a sadly common occurrence once a MOOC has had a few successful runs. 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). The general lack of staff communicativeness also led to grumbling about the final exam, with numerous discussion posts questioning this or that problem. Fortunately, these lapses did not meaningfully interfere with an excellent learning experience, and can readily be corrected.
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Siying Liu profile image
Siying Liu profile image
10/10 starsCompleted
  • 3 reviews
  • 2 completed
1 year ago
If you only want to take one course to learn data science using R, take this one. What I really like about this course: 1. Interesting real-world cases. 2. Highly hands-on: you can work through all the problems following the guidance, and have experience participating in Kaggle competition. 3. The instruction -- very clear and easy to follow. 4. Covers key areas in data science, and opens door to future exploration.
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Akash Gupta profile image
Akash Gupta profile image

Akash Gupta

10/10 starsCompleted
1 year ago
This course is awesome. I didn't know much about analytics and R but after going through this course I can say that I learned from the very best. This is the best course if someone wants to start his journey into data analysis.
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student

10/10 starsCompleted
1 year, 1 month ago
By far this is the best course I've done on edx. Teaching how to make models in R and solving optimization problems, this course provides 0 to 100 learning experience. I couldn't imagine to learn more from an online course, Now i see why MIT is No.1 worldwide, it's no coincidence.
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Surabhi Trivedi profile image
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Surabhi Trivedi

10/10 starsCompleted
1 year, 3 months ago
This course is spiking my interest in data science. After doing this course I'm seriously considering data scientist as my career option. Undoubtedly the best course on ML i've come across so far. The exercises are amazing and give you a hands on experience on R. Puts forward real word problems and covers the right applications of ML in the industry today.
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Yogesh Kumar Purohit profile image
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Yogesh Kumar Purohit

10/10 starsCompleted
1 year, 3 months ago
This is the best MOOC I have ever attended and the only one till now that I completed fully and attained certificate of completion with 91% scores. My score is just a reflection of how well it was taught and the superior content. I am taking this second time to practice all the topics again.
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Anand Shastri profile image
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Anand Shastri

10/10 starsTaking Now
1 year, 1 month ago
Awesome course. It provides step by step into R and Machine learning concepts. Recommended this course to someone willing to start career in Machine learning.
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