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

5,884 évaluations
856 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

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.

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!

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176 - 200 sur 825 Avis pour Exploration analytique de données

par Thiago M

12 août 2019

Course material and projects help a lot in learning and improving data analysis techniques

par Gat T

27 janv. 2019

I really liked this course. I felt like a learnt a lot especially with all of the projects

par Damian

10 juin 2017

This was the course that I've enjoyed it the most. Especially the data visualization part


22 avr. 2021

Great course with important lessons. Thanks for the lessons and the information provided.

par Anusha V

30 mai 2017

Amazing course. challenging and you learn a LOT (particularly about graphics tools in R).

par Nishchal S

17 mai 2016

Some functions are so tough to remember but general ideas that i received is wonder full.

par Ray W

22 févr. 2016

Pretty useful introduction to various plotting tools and some deep down insights as well.

par Isaac C

12 mars 2018

Im really learning because of the exercises, they are very efficient!!

congratz coursera

par niki a

13 mars 2017

Nice course! Workload was just about right. clear lectures and well-designed projects.

par Gabriella J

26 févr. 2017

Great course! It have been very useful for my job as data scientist! Thank u very much!

par Andrés D C

27 mai 2021

This is essential for every posterior course, take care of understanding the concepts.

par Tom B

15 janv. 2020

As an auditor, this course taught me to see things more clearly from different angle.

par Кирилл С

23 janv. 2019

This is a great course on plotting data as well as finding underlying patterns in it.

par Nannette O S

16 avr. 2018

Great course. The case studies are extremely helpful as well as the SWIRL exercises.

par Warwick T

15 juin 2017

Very enjoyable course although I think the workload could be more evenly distributed.

par Gioachino B

2 janv. 2017

Excellent training! Lectures are easy to understand and let you focus on the subject.

par Avizit C A

30 janv. 2019

A very good course describing commonly used graphical techniques with good examples.

par Jacques d P

4 févr. 2018

Enjoyed this course. The course content and projects are very relevant to the field.

par Sean N

14 févr. 2017

A nice introduction to operating on Data sets. Clear examples, interesting projects.

par Lluís G

28 nov. 2016

Very good course that provides handy tools for data manipulation and representation

par Pratyush D

16 févr. 2016

Very good course. Explains some very peculiar concepts of PCA and SVD very clearly.

par Susan M

12 oct. 2020

Sound practicum with challenging and comprehensive projects to solidify skill set.

par Shubham S

7 oct. 2019

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

par Pooia L

30 août 2018

I loved it, it improved my R skills in plotting and also gave me nice insights!!!

par Pradeep S

21 janv. 2018

Very useful subject on churning data to derive meaningful and actionable insights