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
5,687 évaluations
821 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

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!

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.

Filtrer par :

726 - 750 sur 792 Avis pour Exploration analytique de données

par Rohit K S

Sep 20, 2020

Nice!!

par Tae J Y

Apr 01, 2017

Good!

par Edward A S M

Dec 05, 2019

Good

par 木槿

Nov 02, 2018

good

par Anup K M

Sep 27, 2018

good

par Isaac F V N

Apr 19, 2017

Nice

par Chan E

Mar 22, 2016

nice

par Adur P

Dec 28, 2017

A

par Saurabh K

Apr 27, 2017

G

par Deepak R

Oct 02, 2016

d

par Jose O

Feb 11, 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

Aug 24, 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

Jun 10, 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

Mar 11, 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

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 Haggai Z

Aug 27, 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

Apr 26, 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

Dec 29, 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

Mar 01, 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

Mar 09, 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

Mar 30, 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

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 Ashish T

May 05, 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

Mar 10, 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

Oct 13, 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.