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Avis et commentaires pour l'étudiant pour Exploration analytique de données par Université Johns-Hopkins

4,894 notes
696 avis

À propos du cours

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....

Meilleurs avis


Sep 24, 2017

Very good course! It provide me the foundation in learning how to plot and interpret data. This will definitely strengthen my "R programming" to generate publication type figure for my genomics data!


Jul 29, 2016

This is the second course I have taken from Roger Peng and both were outstanding. I have a strong math background, but not much of a background in stats, but this course was very approachable for me.

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601 - 625 sur 669 Examens pour Exploration analytique de données


Oct 26, 2017

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

par Hyun J K

Apr 17, 2018

Great lecture. I hope there were more assignments. (1 per a week maybe).

I learned many statistical concepts and rcodes by taking this course.

Thank you:)

par Jha A

Apr 19, 2018

very good to understand .

par Praveen k

Oct 02, 2018

Nice course


Sep 20, 2018

More instructions on SVD/PCA would be welcome

par Dai Y

Aug 09, 2018

Improvement should be done to the materials of Week 3.

par Gao Q

Jul 23, 2018

Great content for beginners to get familiar with various graphic tools in R

par Shreya S

Apr 16, 2019

A great course to begin with Exploratory Data Analysis. It teaches you how to analyse data and generate visual reports. However, to actually become efficient at Data Visualization one needs to dig deep and make use of other resources apart from this course. Also K means clustering and other types are explained well in this course but it would have been useful if there were exercises to help implement it in some real problem. Overall this course leaves you confident and enthusiastic about Data Visualization.

par Eric J S

May 29, 2019

Best of your courses yet. Doesn't suffer from difficulty spikes when you hit the projects.

par Piyush D

May 15, 2019

Awesome course ! It reaches you the crux of exploration of data . Although the SVD section could have been more thorough and detailed.

par Anang S A

Jul 16, 2019

this course is more about creating chart for EDA, need more material for reading/interpreting the charts

par Joe D

May 19, 2019

Some of the links in the lectures are out of date, the forums usually have an updated link though.

par Ross D

Sep 04, 2019

Was a little perplexed that we did not address clustering at all in the assignments.

par claire b

Sep 10, 2019

Course gives thorough introduction to basic tools for exploratory data analysis, including visualisation, PCA and clustering. Good mix of lectures, practical in swirl and programming assignment. Swirl practice are mostly a repetition of the examples in the presentations, which is a bit of a pity...and I missed a programming assignment on cluster analysis/PCA

par Jean-Philippe M

Jun 16, 2019

More practical exercises using ggplot2 and clustering would be beneficial. Maybe need to be a 8 weeks module.

par Manuel M M

Sep 27, 2019

It is a good course but in my opinion it is basically support with the R swirl() guide

par Luiz E B J

Oct 03, 2019

I would rate 5 if the course wasn´t so focused on graphic analysis. But, even Like that it´s a very good experience.

par kajal s

Oct 03, 2019

Exploratory data analysis is a very important skill and it is a very good course to learn it.

par Nilrey J D C

Oct 07, 2019

This is a good introduction to do EDA using R.

par Francisco M R O

Jan 08, 2019

The third and fourth week were a big leap in knowledge and not really well explained, for me.

par Mohammad A A

Mar 11, 2019

It was a very useful course with some meaningful homework. My only criticism is that sometimes the theory and the practice are not well connected. Particularly the discussion of PCA, hierarchical clustering, k-means clustering and others. It would be benefit by providing more meaningful reading for those interesting in better connecting the two

par Ashish T

May 05, 2018

Great introduction to the plotting libraries in R and visualization of data.

However the introduction to hierarchical clustering, and Principle component analysis was extremely vague.

par Ray O C

Dec 29, 2016

The first two weeks were good. The third was a bit confusing and the 4th one just felt like padding. A more in depth study of ggplot would probably be more beneficial as I felt like we were only scratching the surface with it

par Asier

Mar 10, 2016

The course content applies to R. The teachers focused on the programming language rather than the application of the existing graphs to explore data.

par Guy P

Mar 26, 2016

It misses an assignment which will allow to practice the clustering skills.