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

4.7
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5,254 évaluations
748 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.

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676 - 700 sur 718 Avis pour Exploration analytique de données

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.

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 Ozanb

Feb 05, 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

May 12, 2016

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

par Katharine R

May 03, 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

Mar 06, 2018

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

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 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 Rafael A

Mar 23, 2017

First two weeks are too repetitive with other courses

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 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.