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Avis et commentaires pour d'étudiants pour Exploration analytique de données par Université Johns-Hopkins

5,922 évaluations
865 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

23 sept. 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!

28 juil. 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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676 - 700 sur 835 Avis pour Exploration analytique de données

par kajal s

3 oct. 2019

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

par Eric J S

29 mai 2019

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

par Gerardo M F G

15 nov. 2020

PCA and SVD are not included in any assignment, it will be great if they are in the future

par Huang-Hsiang C

9 juin 2020

Plotting is very usefulIt would be great to have a step by step breakdown of PCA and SVD.

par Mark F

5 juil. 2017

SVD could be explained a little better i think. I am still not exactly sure how it works.

par Irmgard T

23 juil. 2017

great course...though I would have preferred less focus on cluster and k means analysis.

par Manuel M M

27 sept. 2019

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

par Ross D

4 sept. 2019

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

par Prathamesh

9 juil. 2017

SVD & PCA videos need improvement in terms of background knowledge and understanding

par Migdonio G

9 avr. 2018

You should give more datasets for independent practice! Something we can play with.

par Sawyer W

1 août 2017

Good course. Mostly focuses on how to visualize statistics from the data quickly.

par Shuwen Y

11 juin 2016

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


1 juin 2021

Only the 3rd week was confusing but the confusion was revoked by swirl package

par Philip E W J

21 mars 2017

Would have needed a litle more in depth explanation of the clustering analysis

par Subramanya N

12 déc. 2017

ggplot should have been given more emphasis. It warrants a course on its own!

par Greg R

30 mai 2016

Pretty good course. Nice content. Middle section on clustering felt random.

par Pavel B

18 févr. 2016

I like the course and it was helpful in understanding how graphics work in R.

par RobinGeurts

21 févr. 2019

End assignement was relatively easy compared to the examples in the lectures

par Mario S P G

17 sept. 2018

Good beginners course with helpful tools to take a first glance to your data

par Polina

25 avr. 2018

Nice course, very useful. I wish the links were updated more often, however.

par Jan W v d L

14 févr. 2021

Learned a lot, the cluster and kmeans could have been more explained though

par Gao Q

23 juil. 2018

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

par Mario P

20 janv. 2018

I suggest to shift a little more the focus on svd and clustering techniques

par Olga H

22 sept. 2017

Good course, would have likes more practice & testing on the clustering stu

par Andrew W

19 mars 2018

Challenging but great fun and really helped me to get more familiar with R