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What are MOOCs?

How do I register?

To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.

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.

23 out of 24 people found the following review useful

3 years ago
**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
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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.

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.

a month ago
**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, thi
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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.

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.

8 out of 8 people found the following review useful

2 years ago

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 bes
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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.

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.

1 out of 1 people found the following review useful

a year ago

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 proble
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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 out of 1 people found the following review useful

2 years ago
**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 prob
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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.

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.

3 out of 3 people found the following review useful

4 years ago
**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 be
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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.

2 out of 2 people found the following review useful

2 years ago

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.

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 out of 2 people found the following review useful

3 years ago
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.

1 out of 1 people found the following review useful

2 years ago
**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.

2 out of 2 people found the following review useful

4 years ago
**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.

Modeling (i.e linear regression, logistic regression etc.) are well explained by examples using R.

2 out of 2 people found the following review useful

4 years ago
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.

1 out of 1 people found the following review useful

2 years ago
**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.

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

1 out of 1 people found the following review useful

3 years ago

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.

7 months ago

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.

1 out of 2 people found the following review useful

4 years ago
**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 out of 1 people found the following review useful

4 years ago
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.

2 out of 2 people found the following review useful

2 years ago

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.

1 out of 1 people found the following review useful

2 years ago
**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 out of 1 people found the following review useful

2 years ago
**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.

a year ago
**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
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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.

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.

2 months ago

The strength of this course is its broad selection of USE CASES compared to other data analysis MOOCs, some of which have a more technical focus. If you are interested in getting a taster of what analytics can do, willing to play with a bit of code but do not wish to spend much time cleaning data, this offering from M
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The strength of this course is its broad selection of USE CASES compared to other data analysis MOOCs, some of which have a more technical focus. If you are interested in getting a taster of what analytics can do, willing to play with a bit of code but do not wish to spend much time cleaning data, this offering from MIT Sloane is worth investing in.

As a beginner to R, with little coding background I found the pace fine. I did need to spend a bit more than the suggested 10-15 hours / week to get though all the material, although you can be selective and only finish parts of the syllabus to complete the course. Some of the homework activities are not as well-constructed as others, this was probably to take into account the diverse student level. Community discussion was vibrant.

As a beginner to R, with little coding background I found the pace fine. I did need to spend a bit more than the suggested 10-15 hours / week to get though all the material, although you can be selective and only finish parts of the syllabus to complete the course. Some of the homework activities are not as well-constructed as others, this was probably to take into account the diverse student level. Community discussion was vibrant.

3 years ago
**completed** this course, spending **12 hours** a week on it and found the course difficulty to be **medium**.

I'm changing my focus from Systems Analysis to Analytics. This course fulfilled its proposal and the Competition was a highlight. The data arrive already cleaned and treated, this is not the focus of the course. R basic and functional , it was also very good. The structure is balanced and would have had more content of Optimisation.

3 months ago

is taking this course right now, spending
**7 hours** a week on it and found the course difficulty to be **medium**.

It's one of the best courses for introduction to data science including machine learning. The course is taught using R. I took Python courses before. I was thinking that it would be difficult for me to learn data science using R. But, it proved to be amazing. I learned so many things.

a year ago
**completed** this course.

A fantastic course to do for anyone interested in analytics!

I am a developer by trade with a strong background in maths and I loved the course.

Can be time consuming compared to alot of MOOCs but it worth the effort in the end to stick at it.

I am a developer by trade with a strong background in maths and I loved the course.

Can be time consuming compared to alot of MOOCs but it worth the effort in the end to stick at it.

2 years ago

This course gives wonderful introduction to machine learning techniques. There are several case studies discussed as part of the course which give a good understanding of data analysis and application of various algorithms.

8 months ago
**partially completed** this course, spending **12 hours** a week on it and found the course difficulty to be **easy**.

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.

a year ago

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

I learned more than I expected, definitely a course to take.

I learned more than I expected, definitely a course to take.

3 years ago
**completed** this course, spending **10 hours** a week on it and found the course difficulty to be **hard**.

1 out of 4 people found the following review useful