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

4.7
étoiles
5,505 évaluations
789 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

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.

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!

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726 - 750 sur 760 Avis pour Exploration analytique de données

par Erkan E

Jun 24, 2016

I wish there several comprehensive examples of exploring some real data as guided by the course instructors.

par Mehrdad P

Aug 26, 2019

The course was overall ok, but I wish discussions about k-means, PCA and SVD were divided into two courses.

par Daniel P

Dec 08, 2019

I've learned plotting in R. I expect to learn more in four weeks of "Data Science" specialization.

par Stuart A

Jul 18, 2020

Course hasn't been updated in a long time, some of the data needed for the projects has migrated.

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 Sandeep D

Mar 10, 2018

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

par Guy P

Mar 26, 2016

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

par Alex s

Jan 17, 2018

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

par Amit O

Sep 30, 2017

faced many technical difficluties in pratcice exerices in swirl

par Eduardo V K

Jun 28, 2020

There seems to be some outdated info in several tests.

par Rafael A

Mar 23, 2017

First two weeks are too repetitive with other courses

par Kevin F

Jul 15, 2020

pretty brief and basic. no assessment on clustering.

par Erwin V

Mar 12, 2016

Interesting stuff, but not a lot of detail

par Lidiya G N

Apr 29, 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 Jesús A P G

Jul 21, 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

Jun 07, 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

Jan 31, 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

Feb 06, 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

Mar 26, 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

Dec 14, 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

Feb 06, 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 Freddie K

Apr 05, 2017

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

par Tamaz L

Apr 05, 2016

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

par Desmond W

Oct 19, 2016

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

par Esther L

Aug 22, 2019

Too weak regarding the clustering methods, very disappointed.