Exploratory Data Analysis

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6/10 stars
based on  12 reviews
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FREE

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4721 reviews

Course Description

Learn the essential exploratory techniques for summarizing data. This is the fourth course in the Johns Hopkins Data Science Specialization.
Reviews 6/10 stars
12 Reviews for Exploratory Data Analysis

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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Nirav Desai profile image
Nirav Desai profile image
10/10 starsCompleted
  • 9 reviews
  • 9 completed
3 years, 9 months ago
I found the exploratory techniques described in this course to be useful. The course content was well explained and I found the notes to be useful in completing the assignments.
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Student

4/10 starsCompleted
4 years, 1 month ago
This is a pretty poor show and lazy effort by the suppliers. The assignments don't cover most of the material, and seem to be a token attempt to have something to test. I got full marks BTW - so hopefully not a sour grapes thing, more a disappointed concern that my time could have been better spent. Although some of the later material covers e.g. clustering, the (much earlier) assignments really test how to extract data and plot graphs. The problems - such as they are - may be because of the 'learning opportunity' presented by reviewing the work of others, which is the basis of getting the marks needed to pass. This may have learning benefits, but is the cause of a number of issues. Firstly this front-loads all of the assignments and evaluations to the start, hence missing the opportunity to test the advanced and more important material. Secondly there are the vagaries of peer assignment, with all the vague misunderstandings that ari... This is a pretty poor show and lazy effort by the suppliers. The assignments don't cover most of the material, and seem to be a token attempt to have something to test. I got full marks BTW - so hopefully not a sour grapes thing, more a disappointed concern that my time could have been better spent. Although some of the later material covers e.g. clustering, the (much earlier) assignments really test how to extract data and plot graphs. The problems - such as they are - may be because of the 'learning opportunity' presented by reviewing the work of others, which is the basis of getting the marks needed to pass. This may have learning benefits, but is the cause of a number of issues. Firstly this front-loads all of the assignments and evaluations to the start, hence missing the opportunity to test the advanced and more important material. Secondly there are the vagaries of peer assignment, with all the vague misunderstandings that arise (if the discussion forums were a guide). Thirdly, there are the apparently random and inconsistent submission methods - which are really a test of self-organisation and paying attention to the instructions. I suppose my final criticism is the removal of the Statement of Accomplishment, and the commodification and push towards the paid-for certificates. Given the (lack of) quality of this module, the emphasis away from knowledgeable assignment evaluation, and the problems some students had formalising the signature track process (perhaps not JHU's fault), this is something I will be wary of in the future. From my experience of other MOOCs there is no reason this could not be significantly improved, test students on useful 'stuff', and award a mark on the basis of the subject stated in the title.
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Farid Ahamat profile image
Farid Ahamat profile image
6/10 starsTaking Now
  • 1 review
  • 0 completed
4 years, 4 months ago
Nothing that you can't Google, the only advantage taking this class is it provides the structural in learning, making the whole process easier. That being said, the content only touches up to the intermediate level of creating a graph in R. It's up to the student to harness his knowledge from this class to better in R's graph. Week 3 is totally crap. It starts to touch on clustering and distance, concepts that requires advanced math knowledge, and also you need real world experience to know where/how/why these concepts relates to the graphing elements.
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Ramiro Aznar profile image
Ramiro Aznar profile image
8/10 starsCompleted
  • 27 reviews
  • 26 completed
4 years, 6 months ago
One of the best MOOCs I have taken about data analysis and graphics. In my humble opinion, you should have passed some previous courses or tutorials on R to take this one. But because it is a course which is offered in a wide variety of dates, you can prepare well for the next one. The videos are quite useful, the instructor as usual and the quizzes and practices quite good as well.
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Student

4/10 starsCompleted
4 years, 7 months ago
the instructor is a bad lecturer. Too many ughms and so's. He is also not clear enough. I give two stars for the content. There's a lot of it, but it is not explained or presented well. There is just too much stuff to take in, and the instructor doesn't even try to really help you. So you do get a lot of information, but you get it on the slides, the same way you can google things or read a book. You don't need a course for that.
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Antoine Freches profile image
Antoine Freches profile image
4/10 starsCompleted
  • 5 reviews
  • 3 completed
4 years, 9 months ago
The course focuses on making plots. In my opinion there is too much emphasis on this. This is something that you could pick up online once (and if) you need to plot something in a specific way. In the contrary, the parts on clustering and SVD/PCA are treated very quickly and without a real explanation on the underlying concepts.
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Hamideh Iraj profile image
Hamideh Iraj profile image
6/10 starsCompleted
  • 70 reviews
  • 60 completed
5 years, 1 month ago
This course is on the acceptable borderline. The quizzes were quite easy, not challenging. The same for the two course projects. The drawbacks are: 1\. The course does not evaluate your understanding of weeks 3 and 4. No motivation to watch them carefully. Only weeks 1 and 2 have quizzes. 2\. Doing two course projects is irritating. You have to do the main project and peer assessment in four weeks. So you have to keep an eye on deadlines for four weeks. To wrap up, I preferred the course with full quiz coverage, more practice on the three plotting systems and one course project.
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Jeff Winchell profile image
Jeff Winchell profile image
6/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 8 months ago
I liked learning how to quickly make useful graphs for this class. I'm not exactly keen that R has 3 disparate systems for doing standard graphs (2 would be more than enough), but since I was barely tested on one of them, that was OK. I do think the assessment strategy is a bit odd, where you are not assessed on about half the material.
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Greg Hamel profile image
Greg Hamel profile image
7/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 5 months ago
Exploratory Data Analysis is the 4th course in John Hopkins’s data science specialization track. I'm writing this review after completing all the lectures and quizzes; I'm not planning to complete the projects. The first 2 weeks of this course provide a thorough overview of plotting in R using the base graphical package, the lattice package and the ggplot2 package. Week 3 takes a sudden detour into data clustering and the fairly advanced topics of principal components analysis and single value decomposition only jump back to plotting with a section on color. The clustering section seems a little about of place since there is not any introduction explaining the purpose of clustering. What's worse the SVD and PCA sections require a fairly high level of linear algebra knowledge to understand, which are not prerequisites for this course. I suspect that section will leave may students scratching their heads. Week 4 consists of 2 case stud... Exploratory Data Analysis is the 4th course in John Hopkins’s data science specialization track. I'm writing this review after completing all the lectures and quizzes; I'm not planning to complete the projects. The first 2 weeks of this course provide a thorough overview of plotting in R using the base graphical package, the lattice package and the ggplot2 package. Week 3 takes a sudden detour into data clustering and the fairly advanced topics of principal components analysis and single value decomposition only jump back to plotting with a section on color. The clustering section seems a little about of place since there is not any introduction explaining the purpose of clustering. What's worse the SVD and PCA sections require a fairly high level of linear algebra knowledge to understand, which are not prerequisites for this course. I suspect that section will leave may students scratching their heads. Week 4 consists of 2 case studies where the professor shows you how to perform an exploratory analysis on a couple different data sets. If this course only consisted of the plotting lectures I’d give it a 4 out of 5. The plotting lectures that make up the bulk of the course are well done and this course provides more instructor face time and live examples in R than any of the 3 courses in the first wave of the data science track. Unfortunately, there are no interactive exercises or in-lecture quizzes and the principal components analysis and single value decomposition sections are too advanced for this course. It would have been better if they left the SVD and PCA functions as black boxes in R and simply explained in general terms what they do and how to interpret their output. Still, the quality overview of R plotting makes this course worth a look.
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Tamás Nagy profile image
Tamás Nagy profile image
10/10 starsCompleted
  • 7 reviews
  • 7 completed
5 years, 3 months ago
This was a really informative course from a really good teacher! I learned a lot!
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Richard Taylor profile image
Richard Taylor profile image
1/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years, 4 months ago
How can I describe this? The first two weeks are a chaotic overview of plotting in R. Three plotting systems are described but none of them is covered in enough detail. You could just skip the videos and Google how to plot different things. The last two weeks seem to be just filler material, clustering and dimensionality reduction appear out of the blue but there are no quizzes or assignments about those topics so you could as well skip them too. So at the end you have to do a couple of programming assignments about plotting in R with the help of Google and that's it. The examples and assignments are really non-interesting and unchallenging unless you are really into air pollution. It's bad, very bad.
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Marcelo Soares profile image
Marcelo Soares profile image
8/10 starsCompleted
  • 16 reviews
  • 13 completed
5 years, 4 months ago
The first two weeks deepen part of what mr.Peng used in his "Computing for Data Analysis" course. He's a great explainer. If you follow the videos up with doing step-by-step the shown code, you'll get the whole basis to do it on your own. Initially, I got a little worried about having two course projects. They are challenging enough, though - they don't require more than you should know by the end of the first week, in the first project, and by the end of the second week, in the second. (In other courses, with other professors, the project was on the third week and you had to use concepts you'd only have on that week.) I didn't give it five stars yet because I'm yet to see how mr.Peng explains clustering. I've seen it with a more confused professor before and that was the point I dropped the course. Watch this space.
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