Learner Reviews & Feedback for Exploratory Data Analysis by Johns Hopkins University

Graphs and plotting is at the heart of data analysis and data science, and without it you would have difficulty conveying ideas, and having graphs to explain numerical/statistical data is always handy. Visual representation of a data set, and using visual cues to gain an understanding of data, can save a lot of time, and can help you gain additional insights into the data. This course teaches you key techniques on how to apply some graphing and plotting methods to visually explore data, and it does so really well and in great detail, and also provides some good demos.

I was able to learn the material presented over the time of the course. It's a lot of material to cover in the time I could commit to it but I feel confident using the tools and methods presented. The projects were very valuable both from getting to practice the methods and tooling and also from seeing how other students approached the solutions. I really helped put all the options into context and highlighted the value of using the different tools and where to use them. Only knock would be sometimes the background noises in the videos were distracting.

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!

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.

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.

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.

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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.

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.

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.

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 :)

This is a great course from Johns Hopkins University. I learn a lot through this course, from creating better color palettes to clustering data. The last project is interesting, in which I have to apply my R programming skills, data visualizations, and explore some other features in ggplot2 to solve the questions. Thank you Coursera and John Hopkins University.

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!

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

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.