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.

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- Week 1 -
**Week 1**

This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already. - Week 2 -
**Week 2**

Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualizing data, it was par... - Week 3 -
**Week 3**

Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for exploratory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dime... - Week 4 -
**Week 4**

This week, we'll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data. How one goes about doing EDA is often personal, but I'm pr...

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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4 / 5

Terminé

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

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5 / 5

Terminé

Highly recommended course for budding data scientist. I loved the John Hopkins univ pedagogy and peer review system. The content is great.

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2 / 5

Terminé

This is the worst of the Data Science courses so far (they've all been pretty good up to this point).It's called Exploratory Data Analysis, but is actually all about the graphics systems in R. And it does a botched job on those as well.All quizzes and assignments are about the graphics systems. The only portion of the course that deviates from that is Week 3 (for which there is no quiz or project) where we "learn" about clustering and dimension reduction. However, that material is presented really poorly: not enough depth for someone who is already familiar with the subject matter; and not nearly well enough explained for newbies.On the graphics side, none of the systems is explored in great depth. The lattice system is essentially just mentioned in passing. To cap it all off, the brief for the last assignment is really ambiguous, which often causes perfectly valid work to be graded poorly by peers. (Just look at the forums, if you need proof.)

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5 / 5

Terminé

This was a great course. I learned how to use several graphic systems within R, and to imagine how to make clear answers to questions using plots.

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4 / 5

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It's a very good course. Week 3 was a little bit more challenging than expected, as well as assignment 2, but you get a good idea of how to use all the different plotting systems

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5 / 5

Terminé

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.

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5 / 5

Terminé

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.

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4 / 5

Terminé

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.

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5 / 5

Terminé

Very good introduction to Exploratory data analysis for beginners, the material is structural, lectures are interesting and useful

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4 / 5

Terminé

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.

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