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

4.7
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5,904 évaluations
860 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
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!

CC
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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626 - 650 sur 829 Avis pour Exploration analytique de données

par Ashish25 S

1 avr. 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 Morbo

27 mars 2018

The course was great, I'm not sure if I'd really consider using the base plotting package in reality as the plots are just too ugly, and the API is harder to learn. I think a stronger on ggplot would help to keep it relevant.

par Connor G

14 août 2017

I enjoyed the course and learned important graphing concepts for R/RStudio. I just wish the assessments had been a little more rigorous, as it felt like I could have done better but still passed the projects anyway.

par Greg A

22 févr. 2018

This is a very good course, at times it felt like the instruction was to do things mechanically without understanding the motivation. Perhaps this should come after or in conjunction with Statistical Inference

par Carlos R

28 mai 2017

I love the course. However, the treatment of PCA, SVD, and colors seems to me very long and slow. Maybe a more direct and quick overview would be better. Even with that expection I really enjoy the course.

par Ben K

27 déc. 2020

It was fun and interesting learning how to explore the data. For the final project I missed a assignment about clustering, PCA and SVD. It could be useful for a better understanding of the concepts.

par Bill S

21 juin 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 Jukka H

14 juin 2020

Great in-depth content about techniques related to exploratory data analysis and implementation in R language using R Studio. Definitely recommend this course to any aspiring data scientist!

par Raviprakash R S

13 févr. 2017

Nice course, but too much focus on "R" as a tool.... Industries don't use R as much... The course must be made more generic and independent of R - understand it is not easy to do but ....

par Luke S

31 oct. 2019

Good introduction. 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.

par Ng B L

9 mars 2017

When it comes to hierarchical and K-means clustering, the theory wasn't explained clearly. When do we use U and V for what purpose? How does D come in? I'm left confused after this.

par Štefan Š

17 avr. 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.

par Diego P G

7 janv. 2018

It's a very good course. Week 3 was a little bit more challenging than expected, as well as assignment 2, but you get a good idea of how to use all the different plotting systems

par Christian B

11 déc. 2016

The course is interesting and the content is relevant. I do think that there are some issues with project 2 though. I did provide feedback on that to the course administrators.

par Hernan D S P

6 mars 2018

I learned a lot on this course, it helped me to understand and identify some of the situations I experience at work. Totally recommended if you want to apply it right away.

par Terry L J

18 oct. 2018

Seems this would type of course in an online learning MOOC would be better if it was more direct hands on "how to" and less focused on explanatory fluff (academic style) .

par Igor T

30 janv. 2017

Good introduction to patterns recognition. I found principal components analysis technique very useful. It would be great to provide more lectures about this topic.

par Carlos G W

6 sept. 2020

I enjoyed the course and learned a good deal. However, the level of challenge of the projects is much higher than the scant explanation provided by Dr. Peng.

par DESIREE P

19 avr. 2021

We learn very useful things. However, there is little emphasis on the statistical part (singular value decomposition) which I think deserved more exercises.

par Diego T B

17 nov. 2017

Interesting. But I would prefer the differences between comparison plots. What do they are useful and why is it better to plot with bars rather than lines.

par Robert W S

14 févr. 2016

A quiz or project question on k-means clustering or PCA would be nice. Overall the course provided solid coverage of the three main plotting systems in R.

par Guillaume S

8 juin 2018

Interesting course to know plotting systems and to have a first view on clustering and dimensions reduction. This part should be however more developed !

par Hyun J K

17 avr. 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 Hank C

13 sept. 2020

Course material, lectures, exercises are excellent.

There was not enough theory, and there was too much specific to R and graphing packages covered.

par Robin S

28 févr. 2017

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