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

4.7
4,889 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

Y

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!

CC

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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501 - 525 sur 668 Examens pour Exploration analytique de données

par Ryan B

Apr 25, 2018

Good, but the lack of assignment in week 3 seemed to screw up the UI, prompting me continually to do the Swirl exercises, which were non-compulsory (and, given I hadn't completed any of the other Swirl exercises, something I didn't want to take on.)

par Ratnikov Y

May 27, 2017

Clustering is overwhelming field of knowledge.

par Bill S

Jun 21, 2017

The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.

par Pavel B

Feb 18, 2016

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

par Saruul A

Dec 23, 2017

good for building basic foundation

par Phillip K

Mar 20, 2018

Good stuff just as I have come to expect from this University and the courses that are part of this Signature Track.

A great deal of the lectures and work on assignments/quizzes/projects was learning and using the various plotting systems in R. Certainly this is important, but to put it into perspective, I spent four hours creating six plots for the final project, when I was able to use Tableau Desktop to create all six plots in under five minutes.

So formally learning the data exploration techniques was good, but expect much of this course to be about learning the R plotting systems.

That said, there is a point in this course (and the first time for all the courses to this point) where the topic suddenly got very, very technical. When clustering techniques were introduced it felt as if you were turned on your head as the focus suddenly went from various ways of plotting data in R to being neck deep in the explanation of clustering techniques that require a great degree of Linear Algebra knowledge.

Don't panic though. While there are questions in the guided assignments that are difficult, you don't really need to recall all of your Linear Algebra courses from college to pass this course. After all, R "has a package for that."

par Paul M

Jun 02, 2016

Very good introduction to the various graphing systems.

par Greg R

May 31, 2016

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

par Vebashini N

Nov 14, 2017

Thank you, i learnt a lot and will continue on my journey.

par Giovanni M C V

Feb 16, 2016

Excellent course with great didactic. Congratulations!

par STEVEN V D

Dec 08, 2017

Great practical course on exploring big datasets in R. The main part, plotting, is very clearly and thoroughly explained and framed. Only 'single value decomposition' and 'principal components analysis' was somewhat hard te grab and need a lot of extra research and study.

par 贝叶斯统计

May 23, 2016

还不错的R语言绘图入门

par Polina

Apr 25, 2018

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

par Lindy W

Nov 24, 2016

Interesting learning more about ggplot and base plotting system, as well as clustering techniques.

par Bijan S

Jan 30, 2016

The course is useful with a lot of learning.

The second half needs more of improvement, I think the pace is quite fast compared to others.

par Jeremy O

Mar 10, 2017

excellent!

par Robin S

Feb 28, 2017

The course was fantastic. It was very challenging. I could do with some additional opportunities for exploratory analysis to reinforce some concepts.

par Chan E

Mar 22, 2016

nice

par Carlos L

Jun 22, 2016

swirl is very used in this course. It is one of the best tools to learn R

par Prathamesh N

Jul 09, 2017

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

par Irmgard T

Jul 23, 2017

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

par Ashish S

Apr 02, 2017

It was awesome to learn visualization. SVD and PCA part of the course could have been elaborated better, and a pilot project on that would have cleared the basic concept. As usual Prof. Roger is a engaging and amazing teacher.

par Chun-Fu W

Jun 21, 2016

Very interesting and insightful course. I enjoyed it.

Assignment was okay, could have provided more challenge and depth though.

par Zhang S

Jul 09, 2018

Week 3 content is difficult to understand without background knowledge in clustering and component analysis. Hope the instructor can provide some materials or web links for cluster and component analysis at the beginning of Week 3. Other weeks' contents are good and helpful!

par Štefan Š

Apr 17, 2016

I found it very useful.

Some space for improvement are better coding skills (naming variables) and

some more complex topics like SVD / PCA should be explained in a more intuitive way.