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.
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!
par Tejus M•
May 25, 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•
Oct 20, 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: https://www.linkedin.com/in/joseantonio11
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•
Jan 05, 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•
Dec 11, 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 reem z•
Jun 23, 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•
Jul 21, 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 Anirudh J•
Jul 06, 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•
Jan 06, 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•
May 17, 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.
Sep 10, 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•
Nov 21, 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•
Nov 21, 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•
Jan 25, 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•
Aug 12, 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•
Jul 24, 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•
Feb 01, 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•
Oct 03, 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•
Jun 09, 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•
Jul 29, 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•
Jun 08, 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 Rodney A J•
Jun 17, 2017
Great course. This course required us to create multiple plots using different R libraries created for the purpose. Although ggplot2 seems to be very popular, the base plot system and the lattice plot system provide compelling alternatives.
par Adi T•
Jan 21, 2017
It starts to get a little more technical and complicated when I reach Week 3. A lot of things about Dimension Reduction and K-means method. I would love to have some assignments or exercises on that.
Other than that, I love this module.
par Dev P•
Jan 05, 2020
Great course providing a good overview into the various plotting systems in R. I enjoyed the introduction to principal components analysis and singular value decomposition, but could have used more material to practise these methods
par John A R B•
Sep 22, 2018
The exploratory data analysis is a very important part of the elaboration of a data product because this period helps to understand the most important variables and the elements to construct models and visualize an early result.
Dec 14, 2016
One of the best parts is the introduction of Singular Value Decomposition and Principal Component Analysis. Also does K-means and other clustering.
I would recommend reading the handouts to you get the math behind the technique.