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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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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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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5 / 5

Terminé

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.

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4 / 5

Terminé

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.

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4 / 5

Terminé

Interesting. But I would prefer the differences between comparison plots. What do they are useful and why is it better to plot with bars rather than lines.

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5 / 5

Terminé

Excellent course. I learned more than I expected. A technique that was always at hand but never used: perform analysis through graphics exploring countless variables at a single time.

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1 / 5

Terminé

I can't help but feel lied to. The FAQ for the specialization says the following: "We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required). " If no linear algebra background is required, then why do you assume that I know what a singular value decomposition is? Or principal components analysis? Terrible course.

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5 / 5

Terminé

The peer review takes so long..................................................................................... which costs me extra money even though I have finished all the stuff 1.5days before the last day.

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5 / 5

Terminé

Very insightful course!!!The swirl packages and course projects in "Exploratory
Data Analysis" course have really helped me to understand the power of R
in performing introductory graphical analyses towards initial inferences. It
has good hands-on exercises to really put to action various sophisticated
graphs and plots for boardroom conversations on how to go deeper into the data
analysis in order to find meaningful business insights or build powerful
predictive models. As I advance through the specialization, I am getting to
realize how powerful Statistical Learning through R is for quick business
action and automation.

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