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Exploratory Data Analysis

This course is a part of Data Science, a 11-course Specialization series from Coursera.

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.
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Johns Hopkins University

Rating 4.5 based on 665 ratings
Length 5 weeks
Starts Jun 29 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

According to other learners, here's what you need to know

data science in 21 reviews

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*.

This is the fourth course in the Data Science specialization.

Also great stuff to practice previous training on Data Science.

A very useful course for data science beginners.

Learning how to make plots and play with the data is where data science finally started to get fun!

for Crediting the Course Very good for anyone wanting to get into the field of Data Science using R This course is very important for beginners and it is also simple to understand.

Nice course Great first step towards data science Very good material and structure of course!

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exploratory data analysis in 19 reviews

very clear and easy to review and learn ggplot and other useful things This is the worst of the Data Science courses so far (they've all been pretty good up to this point).It's called Exploratory Data Analysis, but is actually all about the graphics systems in R. And it does a botched job on those as well.All quizzes and assignments are about the graphics systems.

Didn't know anything about R Exploratory Data Analysis before but now it seems easy.. the best course ever to understand gg plot It's a very good course.

Thjis one of the best courses gives a great idea about plotting and exploratory data analysis !

!The swirl packages and course projects in "Exploratory Data Analysis" course have really helped me to understand the power of R in performing introductory graphical analyses towards initial inferences.

学到很多实用东西 Great course to learn how to build nice graphics and do exploratory data analysis Very good course!

This was an important class because in future classes in the certification peer reviewed projects require some sort of exploratory data analysis.

Very informative I am using exploratory data analysis almost every time when loading raw data.

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dimension reduction in 10 reviews

This week should first start with a practical example/use of clustering and then move on to technical Very informative course, enhance dat I would have liked an assignment to focus on the clustering methods and I think dimension reduction was reviewed way too quick.

The only portion of the course that deviates from that is Week 3 (for which there is no quiz or project) where we "learn" about clustering and dimension reduction.

Great overview, especially the parts on dimension reduction.

A lot of things about Dimension Reduction and K-means method.

It was a wonderful experience to read the structure of data before delving into the advanced statistical levels of data analysis.The need for inclusion or exclusion of dependent variables or dimension reduction in regression analysis can be intuitively understood and visualized using Data Exploratory techniques and then we have the clue as what to do in the next level.It is like putting the whole characteristic of the data under full control.

Hopefully it could be clearer on dimension reduction.

I am eagerly awaiting the opportunity to apply clustering and dimension reduction on real data in future courses.

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value decomposition in 8 reviews

However, the videos on PCA (principal component analysis) and SVD (singular value decomposition) were difficult to understand, and I had to view several videos on YouTube (e.g., StateQuest or Standord U) that do a far better job of explaining.

If no linear algebra background is required, then why do you assume that I know what a singular value decomposition is?

One of the best parts is the introduction of Singular Value Decomposition and Principal Component Analysis.

However, I wish there was more hands-on or peer-graded practice with K-means, heatmaps, dendrograms, and dimension reduction techniques like Singular Value Decomposition (SVD).

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svd and pca in 7 reviews

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 really enjoyed the sections on SVD and PCA as these really require mathematical maturity.

Help to Better understanding of R-programming graphical Quite repetitious in covering basic graphing, and very shallow in regards of clustering, SVD and PCA.

SVD and PCA part of the course could have been elaborated better, and a pilot project on that would have cleared the basic concept.

great course but wish to have more materials or explanation on svd and PCA part.

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.

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too much in 7 reviews

Very useful subject on churning data to derive meaningful and actionable insights I suggest to shift a little more the focus on svd and clustering techniques It focus too much on the tools and a little bit on the analysis Great course for plotting basics.

Important and interesting stuff - but lots of it is repeated too much, which make it seem like 4 weeks is too much for the material.

I love these courses Useful :D Well Structured Course, Learnt a lot from it Clustering topic is covered superficially, too much time spend on employing ggplot graphs, not very useful since making graphs is straightforward on other software, like excel, once you aggregate datasets correctly.

However, I think it emphasizes too much the lattice and basic plot systems to the point it is redundant with functionality on ggplot.

In my opinion there is too much emphasis on this.

Cons:# Too much focus on hopelessly outdated R functions.# Lectures are mostly powerpoint karaoke along the lines of "You can do that thing.

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Coursera

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Johns Hopkins University

Rating 4.5 based on 665 ratings
Length 5 weeks
Starts Jun 29 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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