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

4.7
étoiles
5,836 évaluations
845 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

Y
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!

CC
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.

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601 - 625 sur 814 Avis pour Exploration analytique de données

par Janet K

28 juin 2017

The pacing of this course was somewhat better than the ones that came before it. I felt that the depth of information covered and the questions asked in the projects and quizzes were a better match than previously. I still let myself take an extra two weeks to complete the final project because I was still learning and playing around with the plots and selection of data, but that was because I wanted to, not because I had to.

par Harshitha H

22 févr. 2016

The course did a good overview of the different plotting systems in R, but it rushed through clustering. I had to watch the videos of k-means and hierarchical clustering at least 3 times to sort of understand it. The matrix concepts went completely over my head. Otherwise, the projects were very interesting, and I would highly recommend this course to other people.

par naghma q

17 déc. 2020

Enjoyed this course a lot. This course allowed me to experiment with and practice various plotting techniques while analyzing the data in the initial stages. SVD and PCA were totally new concepts to me. It would have been better to see some real examples from the field with interpretations instead of understanding these concepts using random numbers' examples.

par claire b

10 sept. 2019

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

par Kalle H

27 nov. 2017

Very good. Great videos but perhaps the most learning was obtained through seing different apparoches taken during the peer review. The course could be even better if more smaller peer reviewed tasks where to be completed where extra points where rewarded for not just displaying correct data, but also visualising it more efficiently.

par doaa e

18 févr. 2020

I'm glad for completing this course, it added a value for me.

I wish the videos about (SVD and PCA) in week 3 was more clear but it was difficult for understand and i feel lost , I think you need to update this videos to have more a satisfied materials.

Thanks for your effort and for what i have learned for this course

par Zhang S

9 juil. 2018

Week 3 content is difficult to understand without background knowledge in clustering and component analysis. Hope the instructor can provide some materials or web links for cluster and component analysis at the beginning of Week 3. Other weeks' contents are good and helpful!

par STEVEN V D

7 déc. 2017

Great practical course on exploring big datasets in R. The main part, plotting, is very clearly and thoroughly explained and framed. Only 'single value decomposition' and 'principal components analysis' was somewhat hard te grab and need a lot of extra research and study.

par Glenn W

2 mars 2019

I really enjoyed this course. I was a good reminder of what analysts need to do when looking at a new dataset. Dr. Peng does a great job walking through the steps and there is enough information given to enable the student to effectively explore on their own.

par Jacques L D K L

16 juin 2020

The course was really good, thanks for that; however the part of single value decomposition and principal components analysis was not explained in a gradual fashion and even though I researched outside of the course I still have some confused concepts there.

par Ryan B

25 avr. 2018

Good, but the lack of assignment in week 3 seemed to screw up the UI, prompting me continually to do the Swirl exercises, which were non-compulsory (and, given I hadn't completed any of the other Swirl exercises, something I didn't want to take on.)

par Guilherme B D J

9 juin 2016

The only missing point I would say about this course is how to deal with skew data and/or outliers. Although it is not specific to "cleaning data", I think there is a good opportunity there to at least give some hints on this subject

par Rashaad J

28 août 2017

The Swirl activities followed along with the lectures, which allowed us (as learners) to better understand core concepts. The lecture videos continue to end while the professor is still speaking, but this is not a major issue.

par Ashish S

1 avr. 2017

It was awesome to learn visualization. SVD and PCA part of the course could have been elaborated better, and a pilot project on that would have cleared the basic concept. As usual Prof. Roger is a engaging and amazing teacher.

par Morbo

27 mars 2018

The course was great, I'm not sure if I'd really consider using the base plotting package in reality as the plots are just too ugly, and the API is harder to learn. I think a stronger on ggplot would help to keep it relevant.

par Connor G

14 août 2017

I enjoyed the course and learned important graphing concepts for R/RStudio. I just wish the assessments had been a little more rigorous, as it felt like I could have done better but still passed the projects anyway.

par Greg A

22 févr. 2018

This is a very good course, at times it felt like the instruction was to do things mechanically without understanding the motivation. Perhaps this should come after or in conjunction with Statistical Inference

par Carlos R

28 mai 2017

I love the course. However, the treatment of PCA, SVD, and colors seems to me very long and slow. Maybe a more direct and quick overview would be better. Even with that expection I really enjoy the course.

par Ben K

27 déc. 2020

It was fun and interesting learning how to explore the data. For the final project I missed a assignment about clustering, PCA and SVD. It could be useful for a better understanding of the concepts.

par Bill S

21 juin 2017

The course on Exploratory Data Analysis was highly enjoyable. I used to do a lot of this sort of thing in my job, but now spend more of my time managing people. It is fun to get "hands-on" again.

par Jukka H

14 juin 2020

Great in-depth content about techniques related to exploratory data analysis and implementation in R language using R Studio. Definitely recommend this course to any aspiring data scientist!

par Raviprakash R S

13 févr. 2017

Nice course, but too much focus on "R" as a tool.... Industries don't use R as much... The course must be made more generic and independent of R - understand it is not easy to do but ....

par Luke S

31 oct. 2019

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.

par Ng B L

9 mars 2017

When it comes to hierarchical and K-means clustering, the theory wasn't explained clearly. When do we use U and V for what purpose? How does D come in? I'm left confused after this.

par Štefan Š

17 avr. 2016

I found it very useful.

Some space for improvement are better coding skills (naming variables) and

some more complex topics like SVD / PCA should be explained in a more intuitive way.