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Exploratory Data Analysis

Exploratory Data Analysis

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8.9/10 (Our Score)
Product is rated as #9 in category R

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. The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life–long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Instructor Details

Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. He received his Ph.D. in Statistics from the University of California, Los Angeles and is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental data. He is the recipient of the 2016 Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to health statistics. He created the course Statistical Programming at Johns Hopkins as a way to introduce students to the computational tools for data analysis. Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. His research is highly interdisciplinary and his work has been published in major substantive and statistical journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. Dr. Peng is the author of more than a dozen software packages implementing statistical methods for environmental studies, methods for reproducible research, and data distribution tools. He has also given workshops, tutorials, and short courses in statistical computing and data analysis.

Specification: Exploratory Data Analysis

Duration

15 hours

Year

2015

Certificate

Yes

Quizzes

Yes

50 reviews for Exploratory Data Analysis

4.5 out of 5
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  1. amir s

    very good course

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  2. Gary T

    I learned a lot, this course more than others, really expanded my competence and appreciate for the capabilities of R, especially is visualization toolbox.

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  3. Henrique R G

    Thank you for opportunity to improve each day.

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  4. Esther L

    Too weak regarding the clustering methods, very disappointed.

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  5. Mehrdad P

    The course was overall ok, but I wish discussions about k–means, PCA and SVD were divided into two courses.

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  6. Farah N

    I enjoyed taking this course specially the projects and swirl practice. If the clustering were a bit detailed, it would be useful. Also we could do a project using the 3 different approaches, it would be interesting. Nevertheless, it was fantastic with the amazing professors.

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  7. FAIZAN D

    The course is very useful

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  8. Arne S

    did not like the swirl–tutorials. they were very tedious and sometimes labelled correct commands as false (e.g. when you typed instead of <– for assigning a value to a variable) also I was surprised that for a beginner programming course in R you had to apply specific functions such as grepl without the function being introduced in the course

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  9. Ross D

    Was a little perplexed that we did not address clustering at all in the assignments.

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  10. Jakub K

    Perfect and thank you

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  11. claire b

    Course gives thorough introduction to basic tools for exploratory data analysis, including visualisation, PCA and clustering. Good mix of lectures, practical in swirl and programming assignment. Swirl practice are mostly a repetition of the examples in the presentations, which is a bit of a pity…and I missed a programming assignment on cluster analysis/PCA

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  12. Angela C

    The BEST course in the series by far. You finally get your hands on some data.

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  13. Muhammad Z H

    Learnt a lot

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  14. Onedio S S J

    Excelent course!!!! Congratulations!!!

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  15. Manuel M M

    It is a good course but in my opinion it is basically support with the R swirl() guide

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  16. Leonie L

    Really good Course!

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  17. David W

    Excellent Course detailing graphics in R

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  18. kajal s

    Exploratory data analysis is a very important skill and it is a very good course to learn it.

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  19. Luiz E B J

    I would rate 5 if the course wasn’t so focused on graphic analysis. But, even Like that it’s a very good experience.

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  20. Ratanaporn

    I am pleased with the success. In completing the course

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  21. Pitak P

    Good

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  22. Shubham S

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

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  23. Nilrey J D C

    This is a good introduction to do EDA using R.

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  24. Nayankumar G P

    Good course for beginners

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  25. Nikhil J

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

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  26. Tristan F

    Roger is the man!

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  27. Robin A

    It was indeed a nice learning through out the course

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  28. Luke S

    Good introduction. The swirl exercises kind of reproduce the lectures though– felt like it might not have been the most efficient use of time to go over the exact same example again.

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  29. Alexander D

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

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  30. Trevor G

    Great course for learning the plotting mechanisms of R

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  31. Johnnery A

    Excelente

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  32. Marcela Q

    Really good if you do not have any background in the field. But even if you do it is a great review

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  33. JiapengSun

    Very good

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  34. Edward A S M

    Good

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  35. Elaine E

    I think this was a great intro course to graphing and exploring data!

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  36. Daniel P

    I’ve learned plotting in R. I expect to learn more in four weeks of “Data Science” specialization.

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  37. Alan C

    Decent overview of the graphing fundamentals in R

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  38. Mathew K

    Great intro to plotting and related tools in R. Will say that the coverage of heatmaps and PCA felt a little out of left field, with very little intuition. However, overall quite good.

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  39. Rahul P

    Awesome!

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  40. Sean S

    Excellent! Very challenging and enjoyable.

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  41. Dev P

    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

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  42. James S

    This was an excellent class.

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  43. Ricardo L

    Excelent! Very useful course

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  44. Amanyiraho R

    Very exciting, Did you Know that just by exploring your data can help you reduce the list of models you want to try out

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  45. ONG P S

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

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  46. Rui W

    Awesome Course!

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  47. Rob S

    Very good resume of the previous lessons, you lear plotting, charts, working with big matrices, create a good practical workflow to understand your first analyses

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  48. Vishal

    this course was very good. Helped me a lot.

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  49. Kunal P

    Little time consuming but a great course.

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  50. Rosa C V

    Me encanto el curso! Buenos profesores, el curso estuvo modulado de la manera interesante y el ingles estuvo facil de entender. La parte practica me motivo a poder continuar con los siguientes cursos de la especializacion.

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