Exploratory Data Analysis

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.

The course on Exploratory Data Analysis was highly enjoyable.

excellent great course for fundamental exploratory data analysis, good starting point for using R to do basic analysis This was the course that I've enjoyed it the most.

I would merge this class within R-Programming section and call it Part 2 rather than categorizing into "Exploratory Data Analysis".

Fenomenal An interesting course Fun and good approach on exploratory data analysis Awesome and must take course!!

Exploratory Data Analysis is one of first steps in every project.

Super useful tutorial of the different plotting systems, and basic exploratory data analysis.

Very accessible yet an extensive overview of techniques in exploratory data analysis.

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!

good learning experience This course is most recommended for Data science practitioner.

Graphs and plotting is at the heart of data analysis and data science, and without it you would have difficulty conveying ideas, and having graphs to explain numerical/statistical data is always handy.

Material teached in this course is must have for everybody who wants to use R for Data Science.

I am looking for a new Data Scientist career.Please, take a look at my LinkedIn profile: https://www.linkedin.com/in/joseantonio11 I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

Its one of the most important steps in learning data science.

Exploratory Data Analysis is the 4th course in John Hopkins’s data science specialization track.

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.

Prof. Peng teaches you not only how to use the r base plotting system but also how to make wonderful graphs using the lattice and ggplot2 packages.

It is better to go through good tutorial over R base plotting system and ggplot2.

The assignments are very easy if you have basic familiarity with R's base plotting system and the "ggplot2" package.

Provides a solid overview of the base plotting system and a discussion (better elsewhere) of others.

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.

I'm glad for completing this course, it added a value for me.I wish the videos about (SVD and PCA) in week 3 was more clear but it was difficult for understand and i feel lost , I think you need to update this videos to have more a satisfied materials.Thanks for your effort and for what i have learned for this course Learned a great deal.

Very useful course Great course providing a good overview into the various plotting systems in R. I enjoyed the introduction to principal components analysis and singular value decomposition, but could have used more material to practise these methods Awesome!

This course teaches how to use three different plotting systems in R. Given the dominance of the tidyverse/ggplot2 paradigm, I really appreciate the opportunity to learn the base plotting system and the lattice plot system.

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.

thank you This course covers plotting (base, lattice, ggplot) then takes a confusing tour into heavy topics of clustering and dimension reduction, then flips back to coloring in charts.

I found principal components analysis technique very useful.

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.

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).

The course is quite good and informative in the first two weeks covering a lot of information and a lot of exercises.Week 3 is very unrelated and hard the videos and exercises are bad, and I had to do this part by myself again.Also when we get to the final course project doesn't cover any of these techniques.In my opinion, week 3 should be replaced with something more related to plotting systems and distributions, also one project would be enough.

The first two weeks deepen part of what mr.Peng used in his "Computing for Data Analysis" course.

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.

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.

Love the case studies really interesting An excellent introduction to R' basic processing and graphing functionalities.

It should focus more on concepts and techniques for delivering richer and meaningful graphics using ggplot rather than talking that much about technicalities on the basic plot and lattice systems.

A more in depth study of ggplot would probably be more beneficial as I felt like we were only scratching the surface with it Excellent course to develop and understand the technique of data visualization.

I felt like a learnt a lot especially with all of the projects Fantastic way to learn graphing systems in R and exploratory analysis This is a great course on plotting data as well as finding underlying patterns in it.

The swirl exercises kind of reproduce the lectures though- felt like it might not have been the most efficient use of time to go over the exact same example again.