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Best of All-Time Online Course

The Analytics Edge

Massachusetts Institute of Technology via edX

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). 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

4.7 rating, based on 80 reviews

Start your review of The Analytics Edge

  • Asr completed this course.

  • Erwin

    Erwin completed 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...
  • Life is Study

    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...
  • 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.
  • Robert Vangen completed 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...
  • Bruno Bakula

    Bruno Bakula completed this course, spending 12 hours a week on it and found the course difficulty to be medium.

    This is one of the best courses on the topic of Analytics/Data Science/Machine Learning that is out there. It is also one of the best MOOCs that I took. Its approach to the mentioned field is the "black box" approach. In other words, the course will not...
  • Ruilin Yang completed this course, spending 7 hours a week on it and found the course difficulty to be very easy.

    The reason I give only three stars is that--- well this scoring thing is purely personal, and personally, I don't favor it too much because I like more technical stuff. As it is offered by the Business school of MIT, for me, taking it might be a wrong...
  • Kim completed this course, spending 17 hours a week on it and found the course difficulty to be very hard.

    I took this class in 2016. I joined this class randomly one day, and it was literally the first time I became aware of data science as a field. The class also hit me at a time in my life that I was ready for some big changes. Over the next several...
  • Robert 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.
  • Anonymous

    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.
  • Anonymous

    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.
  • 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.
  • Anonymous

    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.
  • Anonymous

    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.
  • Anonymous

    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.
  • Aswitala

    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.
  • 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.
  • Anonymous

    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.
  • 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.
  • Anonymous

    Anonymous completed 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.
  • Swap

    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.

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