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 :

776 - 800 sur 831 Avis pour Exploration analytique de données

par Deepak R

2 oct. 2016

d

par Jose O

11 févr. 2016

Insights delivered by the course were great. However, I think it emphasizes too much the lattice and basic plot systems to the point it is redundant with functionality on ggplot. It should focus more on concepts and techniques for delivering richer and meaningful graphics using ggplot rather than talking that much about technicalities on the basic plot and lattice systems.

Assignments were too basic and don't reflect all the concepts learned in the lessons e.g. clustering, which I think are of great interest for researchers.

par Ahmed M

24 août 2016

The course is quite good and informative in the first two weeks covering a lot of information and a lot of exercises.

Week 3 is very unrelated and hard the videos and exercises are bad, and I had to do this part by myself again.

Also when we get to the final course project doesn't cover any of these techniques.

In my opinion, week 3 should be replaced with something more related to plotting systems and distributions, also one project would be enough.

par Andrew V

10 juin 2016

The course covers very limited subset of plots and mostly oriented to R-specific technical routines rather than overall approaches. Case-study example is helpful and contrary to the most comments I do appreciate the final course project: this how most problems are stated in real life. If you would like to cover more fundamental concepts behind exploratory analysis I would recommend other sources.

par Mohammad A A

11 mars 2019

It was a very useful course with some meaningful homework. My only criticism is that sometimes the theory and the practice are not well connected. Particularly the discussion of PCA, hierarchical clustering, k-means clustering and others. It would be benefit by providing more meaningful reading for those interesting in better connecting the two

par Arne S

31 août 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 Haggai Z

27 août 2017

unfortunately this course was not in the same class as earlier courses

cases presented were not interesting or self explained.

concepts were wage and the lectures were boring

i think i need to take parallel course for the same knowledge targets i want to really understand this

par Thomas G

26 avr. 2016

A lot of broken swirl(), which wouldn't be so bad except *a lot* of this course is based entirely on swirl(). Also the swirl() text was almost verbatim of the lectures one has just watched.

All in all, good information, but the swirl() badly needs an update.

par Ray O C

29 déc. 2016

The first two weeks were good. The third was a bit confusing and the 4th one just felt like padding. A more in depth study of ggplot would probably be more beneficial as I felt like we were only scratching the surface with it

par Toby K

1 mars 2016

Excellent overview of plotting and clustering. However, there were a few bits that were required for good completion of the projects that weren't covered in detail. Overall an excellent course and specialization.

par Ralph M

8 mars 2016

Good course overall. There tends to be many lectures that are just lists of commands. Also, they don't seem to be updating the material. Many lectures are several years old and still have typos in them.

par Samer A

30 mars 2018

It's pity that the final assignment doesn't involve the clustering and the principal component analysis. It was quite a demanding topic and I was looking forward to practicing it through solving tasks.

par Fabiana G

23 juin 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 Ashish T

5 mai 2018

Great introduction to the plotting libraries in R and visualization of data.

However the introduction to hierarchical clustering, and Principle component analysis was extremely vague.

par Asier

10 mars 2016

The course content applies to R. The teachers focused on the programming language rather than the application of the existing graphs to explore data.

par Gianluca M

13 oct. 2016

A nice introduction to the three plotting systems in R. The second part is devoted to clustering, but it is not detailed enough to be really useful.

par Andreas S J

4 oct. 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

13 mai 2018

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

par ozan b

5 févr. 2017

Course is good in general but "HIERACHICAL CLUSTERING" part is hard to understand and is not clear, should be explained more.

par Casey B

12 mai 2016

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

par Katharine R

3 mai 2016

Good course, but the SWIRL exercises (and a few quiz questions) needed to be updated for the latest version of ggplot2.

par Johnny C

6 mars 2018

In general was good, but there were some lectures and exercises which were disorganized ("plots with colors")

par Erkan E

24 juin 2016

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

par Mehrdad P

25 août 2019

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

par Daniel P

8 déc. 2019

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