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

5,817 évaluations
842 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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26 - 50 sur 812 Avis pour Exploration analytique de données

par Tarun S

29 avr. 2017

I really appreciate the course design. Even if somebody doesn't have much background in R, she/he can comfortably learn from the videos and understand the concepts. The exercises and project assignments are challenging and actually help you practice and re-visit the lectures and explore further. Though I had already known and used Clustering, PCA and SVD in my work before, I really liked the way these concepts were explained here. I would strongly recommend this course to anybody who is keen to see R in action!

par Amanuel G

5 janv. 2017

It was a wonderful experience to read the structure of data before delving into the advanced statistical levels of data analysis.The need for inclusion or exclusion of dependent variables or dimension reduction in regression analysis can be intuitively understood and visualized using Data Exploratory techniques and then we have the clue as what to do in the next level.It is like putting the whole characteristic of the data under full control.

par Monisha

23 avr. 2020

I strongly recommend this course to anyone who needs clearer understanding of data using Visualizations. The course is well structured and each lecture delivers new concepts in concise format with a very detailed swirl lessons to understand the working of each functions. At the end of the course, I got a completely different view of handling the data and how to extract maximum information from the data to gain meaningful insights.

par Tejus M

25 mai 2018

This course is the first real step from using R for basic data manipulation/stats, to using it for advanced stats. However, the videos on PCA (principal component analysis) and SVD (singular value decomposition) were difficult to understand, and I had to view several videos on YouTube (e.g., StateQuest or Standord U) that do a far better job of explaining. Once I did that, the course videos seemed to make more sense.

par Jose A R N

20 oct. 2016

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career.

Please, take a look at my LinkedIn profile:

I did this course to get new knowledge about Data Science and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors.

par Yusuf E

5 janv. 2018

This course is nice but ggplot should have been given more emphasis probably. I really enjoyed the sections on SVD and PCA as these really require mathematical maturity. Other than that solid introduction to the plotting systems in R which is a must have. This course coupled with Applied Charting with Python will complete my skillset. Looking forward to the rest of the specialization.

par Marcelo S

11 déc. 2017

Great Course. Week 3 requires a bit of mathematical savvy (google SVD/PCA), but since there is no quiz, it won't affect your ability to finish the course, just your ability to fully understand what you are doing. The last project was a bit challenging, which is always good, but most of the information to complete it and earn full marks is in the discussion forums as usual.

par Rimi Z

22 juin 2018

A great course I had to do research on the side to get some ideas and concepts that were presented in this course.... if this was my first course i would have found that not a good thing . However, every time i search i get better as a data science student and i know what to search for and how to find it and i think this is essential if you want to be a data scientist :)

par Jorge E M O

21 juil. 2016

A very good introduction to the exploratory analysis and the R's plotting systems. The most advanced exploratory techniques (singular values decomposition, etc.) are not explained in depth but the overall role that these kinds of statistical learning techniques plays in the exploratory analysis is firmly established.

Great work with the course!

par Nelson S S

21 déc. 2020

Gracias a la Universidad de Johns Hopkins y a los profesores Roger D. Peng PhD,

Jeff Leek, PhD, Brian Caffo, PhD por la generosidad en compartir su conocimiento.

Apreciados estudiantes, Mantenganse activos, mantenganse vigentes y motivados por su aprendizaje

Gracias Coursera por esa gran labor con la humanidad

par Anirudh J

6 juil. 2017

Dr R D Peng is clear, concise and teaches quite systematically so that data visualization and exploration is broken down into its constituent pieces and explained in a way I am yet to come across elsewhere in other MOOCs on the subject. I'm really impressed and happy to have taken up this course.

par Arindam M

5 janv. 2017

A great course. I was hoping to get some more hands on the actual case study though. It was mentioned that Exploratory Analysis is some times intertwined with modeling - and I think in later course it might get covered. But just a glimpse of the relation in the case study would have been helpful.

par Cristóbal A

17 mai 2016

Material de muy buena calidad y a pesar que en ocasiones solo cumple un rol introductorio, el curso no deja de lograr con una simpleza reveladora la construcción de una base solida que luego sirve para profundizar en las herramientas presentadas.

Recomendado 100% y acorde a lo que propone.

par Savitri

10 sept. 2018

Best course to move in the field of Data Science and those who are starting on this field to move towards data science and Machine Learning this is gone help them so much. As the assignment part and the lectures are guided to you this gonna make you feel like best to have this course.

par Roberto D

21 nov. 2016

This class gave me insight on how to better analyze questions. My faults arose when trying to present to much information which may have caused confusion or even disinterest. The main point is to convey results in a simple and understandable manner. Good class lots of practice.

par João F

21 nov. 2017

Excelent course! I learned to make plots with the base plotting system and with the lattice and ggplot2 packages. Challenging assignments. It was great to learn about clustering, dimensionality reduction, SVD and PCA since they play a very important role in Data Science.

par Travis M

25 janv. 2016

A worthwhile course that breaks down methods for doing initial data analysis to get a rough feel of the data. It provides enough useful information about the 3 plotting systems in R and how they differ to allow the student to do sufficient exploration on his or her own.

par Daniel C J

12 août 2016

Loved the course! Super useful tutorial of the different plotting systems, and basic exploratory data analysis. Very practical and hands-on, which is what is needed for this kind of work. Assignments were relatively simple, but I think they got the key points across.

par Jeff A

24 juil. 2018

Great hands on course that will help me with a problem I needed to solve at work today. I’m very excited to start getting into the more real data analysis stuff. All the foundation work in this certificate is awesome and necessary but now the real fun is beginning

par Tad S

1 févr. 2016

If you know some R programming and want to learn how to generate plots for your data analysis, this course will give you a good start. I highly suggest doing swirl exercises after watching the lecture videos to reinforce your understanding of the course materials.

par Nicholas A

3 oct. 2017

I had a lot of experience with graphing data before this class in Mathematica and Excell, however, graphing in R seems so much easier and a lot more fun. This class did a great job of explaining the process, and the assignments felt more like games than homework.

par Keith H

9 juin 2020

Very much enjoyed the discussions about how to initially analyze data before leaping into modeling. I feel I need to continue to look at PCA and SVD and understand them better. They seem very helpful but I feel like I still don't fully grok how to use them.

par Hesham S S E

29 juil. 2020

This course is really essential for any beginner in the field of data analysis as it is not only wokring with the tools, but also it gives you the mechanism of tackling the problems with the right technique of thinking and formulating the right questions.

par John R T

8 juin 2020

Genuinely enjoyed this course. Will be practicing and using what I learned for some time!😊😊😊

If there were anything I would add to the course is maybe a short lecture on how to create 3-Dimensional graphs.

Regards, JT -- See you in the next class!

par Sajal J

12 août 2020

Very good course. I found hierarchical clustering to be very simple and yet so useful. Assignments are also very good because they are based on real data which is often messy and we have to clean and identify keywords/patterns for our analysis.