Chevron Left
Retour à Exploration analytique de données

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

4.7
étoiles
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.

Filtrer par :

801 - 825 sur 829 Avis pour Exploration analytique de données

par sandeep d

10 mars 2018

Excercises are very good. But I believe lecture could be more interesting and easily taught.

par Guy P

26 mars 2016

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

par Alex s

17 janv. 2018

It focus too much on the tools and a little bit on the analysis

par Amit O

30 sept. 2017

faced many technical difficluties in pratcice exerices in swirl

par Eduardo V K

28 juin 2020

There seems to be some outdated info in several tests.

par Rafael A

23 mars 2017

First two weeks are too repetitive with other courses

par Kevin F

15 juil. 2020

pretty brief and basic. no assessment on clustering.

par Erwin V

12 mars 2016

Interesting stuff, but not a lot of detail

par Oscar P G P

17 sept. 2020

It's necessary for more examples!!!!

par Lidiya N

28 avr. 2019

Absolutely No technical help, like insane amounts of homework for each week. People have jobs and businesses to run. Incredibly short duration. Like literally this should have been spread out several more weeks. I would have dropped the class but I can’t. It’s so difficult to get i to the first set of practice assignments and these several sets. Honestly, I am literally getting no help on it and probably won’t pass because I am missing the deadline. I finished 5 coursera courses working on them for 24 none stop. I’ve literally been at this class all day. Besides all the insane amounts of assignments there’s tons of videos to watch and uploads to do. Go buy some books or take another class unless you are unemployed or have nothing better to do.

par Jesus A P G

20 juil. 2020

More than Exploratory data analysis, the course is only focused on how to make graphs in R. That is actually fine, but the name of the course is not suited to the content. In addition, the lectures were too boring. The lack of pedagogy is stunning. The most useful part of the course was the swirl exercises that were the same examples shown in the lectures. That is why it seems that watching video lectures is an incredible waste of time.

par Jamie R

6 juin 2019

Just an extended course on using R. There was little strategies for Exploratory Data Analysis, infact the example jumped from a high level view of the data to then start looking at individual counties. There are multiple tools in the market that will deliver in a better and faster way for exploratory data analysis. This course should be more targeted at developing a skill set that is tool agnostic.

par Joseph M K

31 janv. 2017

Clustering topic is covered superficially, too much time spend on employing ggplot graphs, not very useful since making graphs is straightforward on other software, like excel, once you aggregate datasets correctly. I had not found it very enriching as a course. I would merge this class within R-Programming section and call it Part 2 rather than categorizing into "Exploratory Data Analysis".

par Bartlomiej W

6 févr. 2016

Some parts of material is good quality, but some is bad - also some show bad practices in R. Extensively use swirl as assignments over self work. It is better to go through good tutorial over R base plotting system and ggplot2.

par David I

26 mars 2016

The final project did not require use of the material in the course beyond the first week and a half. I did not take any quizzes or otherwise have my knowledge tested on the material in the second half of the course.

par Rohith J

13 déc. 2016

Course content and assignments were difficult to follow. Loads of statistical content along with high-level R content means it was probably the toughest of the 4 I have taken so far in the Coursetrack.

par Dmitry R

6 févr. 2016

some swirl tests (4,5) don't work because of parameter method in qplot function. This parameter is not realy existed in this function now

par Rahul R

16 juin 2021

S​VD should be better explained. I found diffucilut to understand. Some backgroeund matrices and it's operations should be explained.

par Freddie K

5 avr. 2017

Quite repetitious in covering basic graphing, and very shallow in regards of clustering, SVD and PCA.

par Tamaz L

5 avr. 2016

Very unprofessional, compared to other courses. It wasn't well organized.

par Desmond W

19 oct. 2016

About plotting in R. Not about generating real insights from EDA.

par Esther L

22 août 2019

Too weak regarding the clustering methods, very disappointed.

par ewa b

31 mai 2017

didnt get much useful-- a whole "course" on plotting? meh.

par Vineet P

2 juin 2020

Not upto expectations

par Michal K

10 mai 2016

too superficial