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

4.7
4,950 notes
704 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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651 - 675 sur 676 Examens pour Exploration analytique de données

par Casey B

May 12, 2016

Good class - links and slides have not been updated recently. Frustrating to finish without the exact linkts to the data.

par Fabiana G

Jun 23, 2016

Course feels somewhat abandoned by instructors. Content is okay, but can't help the feeling that it's basically a cash cow - students would benefit a lot if instructors were move involved.

par Andreas S J

Oct 04, 2017

Important and interesting stuff - but lots of it is repeated too much, which make it seem like 4 weeks is too much for the material.

par Dylan P

May 13, 2018

I would have liked an assignment to focus on the clustering methods and I think dimension reduction was reviewed way too quick.

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 Arne S

Aug 31, 2019

did not like the swirl-tutorials. they were very tedious and sometimes labelled correct commands as false (e.g. when you typed = instead of <- for assigning a value to a variable)

also I was surprised that for a beginner programming course in R you had to apply specific functions such as grepl without the function being introduced in the course

par Piyush V

Jun 21, 2016

Veyr boring

par Pamela M

Jun 05, 2016

Alas, after only 10 minutes of the first video, I am reminded that this instructor does not gear his lectures to the true Beginners among us. He speaks much more for an audience of grad students. I do want to complete this Specialization, so I will try again perhaps after learning more - about statistics and R and who knows what else. I fought my way through the first three courses, but now I'm going to work smarter by finding other ways to acquire this knowledge. Then return to him; maybe. This course should be labelled Intermediate and Statistics should be listed as a prerequisite. (I think; since I don't know what it is that I don't know, I am making a guess as to the missing piece of the puzzle.)

par Michal K

May 10, 2016

too superficial

par Desmond W

Oct 19, 2016

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

par Freddie K

Apr 05, 2017

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

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 ewa b

May 31, 2017

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

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 Tamaz L

Apr 05, 2016

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

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 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 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 Esther L

Aug 22, 2019

Too weak regarding the clustering methods, very disappointed.

par Carsten J

Mar 01, 2016

Material is to basic for an entire course.

par Josh H

Nov 05, 2017

I can't help but feel lied to. The FAQ for the specialization says the following: "We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required). " If no linear algebra background is required, then why do you assume that I know what a singular value decomposition is? Or principal components analysis? Terrible course.

par Christy P

Aug 25, 2017

How can you have a course on Graphic Devices and not show one screen shot of a graphic or open R to show how to perform the ideas around this topic?

par Nicholas

Sep 30, 2016

NEEEEEED TO EDIT MY PEER REVIEW FROM OTHERS