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Online Course

Exploratory Data Analysis

Johns Hopkins University via Coursera

(38)
  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Effort 4-9 hours a week
  • Duration 1 weeks long
  • Learn more about MOOCs

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Overview

In this 1-hour long project-based course, you will learn exploratory data analysis techniques and create visual methods to analyze trends, patterns, and relationships in the data. By the end of this project, you will have applied EDA on a real-world dataset.

This class is for learners who want to use Python for applying data visualization and data analysis, and for learners who are currently taking a basic machine learning course or have already finished a machine learning course and are searching for a practical data visualization and analysis project course. Also, this project provides learners with basic knowledge about exploratory analysis and improves their skills in creating maps which helps them in fulfilling their career goals by adding this project to their portfolios.

Taught by

Roger Peng

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

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

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  • 1
Hong X
by Hong completed this course.
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Life S
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.
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13 people found
this review helpful
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Prose S
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
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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Anonymous
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.
6 people found
this review helpful
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Brandt P
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...
1 person found
this review helpful
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Jason C
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
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
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