Chevron Left
Retour à Exploration analytique de données

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

4.7
4,938 notes
703 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!

Filtrer par :

476 - 500 sur 675 Examens pour Exploration analytique de données

par Muhammad Z H

Sep 15, 2019

Learnt a lot

par Santi M

Jun 12, 2019

Good course

par William B B

Jun 12, 2019

Excellent course and applicable in my work right now

par Onédio S S J

Sep 21, 2019

Excelent course!!!! Congratulations!!!

par Nikhil J

Oct 17, 2019

Very useful class, I have already started using the learnings. Its easy and quick to understand.

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 Shubham S

Oct 07, 2019

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

par Nayankumar G P

Oct 07, 2019

Good course for beginners

par Tristan F

Oct 23, 2019

Roger is the man!

par Amanyiraho R

Oct 23, 2019

Very exciting and there is a lot to learn

par Robin A

Oct 30, 2019

It was indeed a nice learning through out the course

par Alexander D

Nov 04, 2019

i very much like the course book. The course book helped me most in really learning the material and how to build plots.

par Trevor G

Nov 09, 2019

Great course for learning the plotting mechanisms of R

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 Marc T

Apr 01, 2019

Great introduction! I am eagerly awaiting the opportunity to apply clustering and dimension reduction on real data in future courses.

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 Daniiar B

Sep 10, 2018

NIce course, but the lectures are a little tedious

par Anup K M

Sep 27, 2018

good

par Soma S K

Sep 29, 2018

Good Place to learn

par Mario S P G

Sep 17, 2018

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