Retour à Exploration analytique de données

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5,904 évaluations

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860 avis

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....

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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par Amit K R

•21 nov. 2017

ok

par Ganesh P

•28 nov. 2017

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par Wei W

•11 sept. 2017

C

par Balinda S

•11 déc. 2016

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par Phillip K

•20 mars 2018

Good stuff just as I have come to expect from this University and the courses that are part of this Signature Track.

A great deal of the lectures and work on assignments/quizzes/projects was learning and using the various plotting systems in R. Certainly this is important, but to put it into perspective, I spent four hours creating six plots for the final project, when I was able to use Tableau Desktop to create all six plots in under five minutes.

So formally learning the data exploration techniques was good, but expect much of this course to be about learning the R plotting systems.

That said, there is a point in this course (and the first time for all the courses to this point) where the topic suddenly got very, very technical. When clustering techniques were introduced it felt as if you were turned on your head as the focus suddenly went from various ways of plotting data in R to being neck deep in the explanation of clustering techniques that require a great degree of Linear Algebra knowledge.

Don't panic though. While there are questions in the guided assignments that are difficult, you don't really need to recall all of your Linear Algebra courses from college to pass this course. After all, R "has a package for that."

par Ruggero B

•29 févr. 2016

My congratulations to all those people who worked to create this course although I have to pick up something I've found a bit annoying:

1- there were two video where the audio were nearly unintelligible

2- I would link the link proposed by the video to be possible to be clicked

3- Some exposition imperfection (even if they make these video more "real and human")

4- Since quiz are not so difficult to be evaluated automatically I found it a bit annoying to notice them locked by not-purchsing, even if I understand there have to be something which would make the customer to purchase.

I've found the swirl experience great although a bit annoying sometimes but I've no clue on how to possibly improve it so.

Keep up with this great work!

Bye

par Jamison C

•4 juil. 2018

You'll learn some cool things like K-means clustering and creating dendrograms, as well as dimension-reduction techniques. The assignments are very easy if you have basic familiarity with R's base plotting system and the "ggplot2" package. I will say I'm very happy with this course in the overviews of R's major plotting systems (though no "ggvis" package), as well as working with color palettes. However, I wish there was more hands-on or peer-graded practice with K-means, heatmaps, dendrograms, and dimension reduction techniques like Singular Value Decomposition (SVD). If these are new to you (they were to me!), you'll certainly walk away from the course more knowledgeable.

par Miguel C

•15 avr. 2020

Once again the teacher was really knowledgeable and engaging. The content was really helpful for my career. The part about clustering was challenging but still manageable. The pacing was good, not too slow (so not boring) but also not too fast (so still easy to understand). The case studies, especially the one about activity measured by smartphones, was one of the best parts of the course.

I didn't particularly enjoy some of the swirl practices. I found some of them to be very very similar (if not the same) as the examples in the lectures, so I only enjoyed the few where there was some new content.

Overall I really enjoyed the course and I would recommend it :)

par Julien N

•13 juil. 2018

A good start for data analysis, this course covers the basics of plotting with the three most common packages (base R, lattice, and ggplot2).I liked the assignment which difficulty is nicely measured (it is not just applying the videos concept, you have to look around the web to find tools and documentation about what functions to use).On a less positive aspects:- I am not sure this course was the best place to introduce kmean and PCA sections...- a lot of content is outdated (wrong links, old R command parameters, ...), look likes a quick freshup update would not do harm given the number of people that keeps registering...

par Ricardo M

•20 nov. 2017

It would be of the best interest to all that the content of the course be reviewed. Seeing references to data from 2012-2015 gives the idea that there's been no recent content review. Although not being the same as taking the full course at the university, this is still a paid training and a certain level of accuracy is expected.

Another note goes to the forums which should be cleansed or handled differently. It's not very helpful to check a forum to see that most of the threads are requiring reviews to the assignments, some from years back.

par Chuxing C

•3 déc. 2015

I have taken the course earlier, so am somewhat familiar with the layout and the materials. Overall it is a very good course and covers a wide range of subject matters. Roger has done a very good job explaining the concepts. I certainly would recommend this course to all who's interested in the subject.

I realize that there's limitation on the time people suppose to spend each week, however, I would like to suggest adding homework, in addition to quizzes.

Several video clips have some audio issues, not sure if that's fixable.

par Joseph F

•3 janv. 2021

I like the case study part which provides you an overview of the practical applications of the skills learned in this course. Learned to use the different types of plotting systems in R which got me to use my 'hacking' skills to experiment and I find it fun. The least I like about the course is the clustering part, mainly because the topic is too advanced for my current level. But I am interested to learn more about it in the future. The swirl lessons were also really helpful to strengthen my understanding of the concepts.

par Shreya S

•16 avr. 2019

A great course to begin with Exploratory Data Analysis. It teaches you how to analyse data and generate visual reports. However, to actually become efficient at Data Visualization one needs to dig deep and make use of other resources apart from this course. Also K means clustering and other types are explained well in this course but it would have been useful if there were exercises to help implement it in some real problem. Overall this course leaves you confident and enthusiastic about Data Visualization.

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

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