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

4.7
4,894 notes
696 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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476 - 500 sur 668 Examens pour Exploration analytique de données

par Muhammad Z H

Sep 15, 2019

Learnt a lot

par Farah N

Aug 28, 2019

I enjoyed taking this course specially the projects and swirl practice. If the clustering were a bit detailed, it would be useful. Also we could do a project using the 3 different approaches, it would be interesting. Nevertheless, it was fantastic with the amazing professors.

par Onédio S S J

Sep 21, 2019

Excelent course!!!! Congratulations!!!

par FAIZAN D

Aug 29, 2019

The course is very useful

par Leonie L

Sep 28, 2019

Really good Course!

par David W

Sep 29, 2019

Excellent Course detailing graphics in R

par Ratanaporn

Oct 03, 2019

I am pleased with the success. In completing the course

par Pitak P

Oct 04, 2019

Good

par Nayankumar G P

Oct 07, 2019

Good course for beginners

par Shubham S

Oct 07, 2019

Thank you so much instructors, the learning curve till now has been great for me.

par anand

Dec 30, 2018

Good course

par RobinGeurts

Feb 21, 2019

End assignement was relatively easy compared to the examples in the lectures

par Glenn W

Mar 02, 2019

I really enjoyed this course. I was a good reminder of what analysts need to do when looking at a new dataset. Dr. Peng does a great job walking through the steps and there is enough information given to enable the student to effectively explore on their own.

par Dai Y

Aug 09, 2018

Improvement should be done to the materials of Week 3.

par Gao Q

Jul 23, 2018

Great content for beginners to get familiar with various graphic tools in R

par Daniiar B

Sep 10, 2018

NIce course, but the lectures are a little tedious

par Terry L J

Oct 18, 2018

Seems this would type of course in an online learning MOOC would be better if it was more direct hands on "how to" and less focused on explanatory fluff (academic style) .

par Mario S P G

Sep 17, 2018

Good beginners course with helpful tools to take a first glance to your data

par LIWANGZHI

Sep 20, 2018

More instructions on SVD/PCA would be welcome

par Soma S K

Sep 29, 2018

Good Place to learn

par Anup K M

Sep 27, 2018

good

par Praveen k

Oct 02, 2018

Nice course

par 木槿

Nov 02, 2018

good

par Julien N

Jul 13, 2018

A good start for data analysis, this course covers the basics of plotting with the three most common packages (base R, lattice, and ggplot2).I liked the assignment which difficulty is nicely measured (it is not just applying the videos concept, you have to look around the web to find tools and documentation about what functions to use).On a less positive aspects:- I am not sure this course was the best place to introduce kmean and PCA sections...- a lot of content is outdated (wrong links, old R command parameters, ...), look likes a quick freshup update would not do harm given the number of people that keeps registering...

par Guilherme B D J

Jun 09, 2016

The only missing point I would say about this course is how to deal with skew data and/or outliers. Although it is not specific to "cleaning data", I think there is a good opportunity there to at least give some hints on this subject