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  • Provider Coursera
  • Subject Data Analysis
  • $ Cost Free Online Course (Audit)
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Effort 4-9 hours a week
  • Start Date
  • Duration 4 weeks long

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Overview

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This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Taught by

Roger Peng

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Reviews for Coursera's Exploratory Data Analysis
3.9 Based on 38 reviews

  • 5 stars 24%
  • 4 stars 61%
  • 3 stars 5%
  • 2 stars 5%
  • 1 stars 5%

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  • 1
Life S
4.0 4 years ago
Life completed this course.
The first 2 weeks of the 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.
13 people found
this review helpful
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Prose S
1.0 4 years ago
by Prose completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
A painful, dull offline course on plotting & clustering in R slapped online with minimal conversion like the rest of JHU's execrable Data Science specialisation*. Hard only due to the appalling pedagogy. (Have these guys heard of labs? Apparently not...)

*Which, tragically, is apparently one of Coursera's top moneyspinners. Sigh.
9 people found
this review helpful
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Anonymous
2.0 4 years ago
Anonymous completed this course.
Another boring course you'll have to slog through. It's half learning a few things about making plots, half topics that been better covered elsewhere (k-mean). You can actually graduate those courses with horrible programming. As usual you'll learn more by surfing stack-overflow than by the videos. I've done half the assignments before looking at the vids.
7 people found
this review helpful
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Brandt P
3.0 3 years ago
by Brandt completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
This is the fourth course in the Data Science specialization. The course covers exploratory analyses in R, primarily making figures using the three most common packages: base R, lattice, and ggplot2. The instructors also manage to throw hierarchical clustering, k-means, and pca into the 3rd week of the course, which seems a little odd as these topics might be better left for the machine learning course. The course ends with a peer-graded course project, similar to other courses in the specialization.

I found this course to be fairly useful, on par with the preceding courses but pe…
1 person found
this review helpful
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Anonymous
1.0 3 years ago
Anonymous is taking this course right now.
A boring and pointless money-generating vehicle from JH. And yes - reviews should be at least 20 words - I wonder if I find a way around that.
5 people found
this review helpful
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Jason C
4.0 3 years ago
by Jason completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
This is a good starting point for any data analysis work, and the course covers the basics, and a bit more, rather well. It's a bit light on what you should do with the information you gather from your data exploration though.
1 person found
this review helpful
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Markus S
4.0 3 years ago
Markus completed this course.
Quite good, quite basic for those who want to review their knowledge. Should be good for those with no previous experience.
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Rafael P
4.0 3 years ago
Rafael completed this course.
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5.0 3 years ago
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4.0 3 years ago
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4.0 3 years ago
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4.0 2 years ago
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