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Volver a análisis exploratorio de datos

Opiniones y comentarios de aprendices correspondientes a análisis exploratorio de datos por parte de Universidad Johns Hopkins

4,573 calificaciones
653 revisiones

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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....

Principales revisiones


Jul 29, 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.


Sep 24, 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!

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1 - 25 de 631 revisiones para análisis exploratorio de datos

por Rok B

May 15, 2019

This course is basically plotting with R and clustering/dimensionality reduction. There's is not enough emphasis on the later in my opinion. The final assignment focuses only on plotting, which is a shame.

por Daniel H

May 13, 2019

Provides a solid overview of the base plotting system and a discussion (better elsewhere) of others. Introduces some higher level exploratory methods, without much information on either the theory or application (simply walks through the recipe). Assessments do not match the lecture material, so the credential is essentially meaningless. Read the associated book, watch the video lectures if you'd like. Don't bother with paying for the certificate.

por Paul R

Mar 12, 2019

This course covers plotting (base, lattice, ggplot) then takes a confusing tour into heavy topics of clustering and dimension reduction, then flips back to coloring in charts. The order of the lectures is confusing and PCA/SVD needs more background, clearer explanation and treatment (gets covered a bit more later under regression). Assignments are good and swirl courses helped solidify the lectures.

por Faben W

Feb 04, 2019

This lesson could have been significantly improved if there was at least one assignment on clustering/dimensional reduction. Those are probably the hardest concepts thought thus far, so it would have been extremely useful to have at least one challenge to work through.

por Dale O J

Oct 16, 2018

This has been a challenging course for me, for whatever reasons. I have devoted a great deal of time in reading Dr. Peng's books as well as reviewing work product of other students to get a better grasp of the logic and methodology. I have enjoyed this course more than any of the preceding courses. And, the struggle I believe will be worth the effort and facilitate my completion of the data science specialization program.

por Roman

Aug 30, 2018


# Too much focus on hopelessly outdated R functions.

# Lectures are mostly powerpoint karaoke along the lines of "You can do that thing. And you can also do that other thing. And also you do this third thing" without much real-world application.

# ggplot2 is the only modern viz package that gets mentioned


# The swirl exercises are great (but very buggy on Mac)

por JM

Jul 11, 2018

Once it got to the clustering section the lessons were inscrutable. Extremely difficult to understand and not explained well.

por Dilyan D

Feb 12, 2018

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.)

por Luca R

Jun 10, 2017

The videos were merely repeating the content from swirl, with absolutely no added values.

por Beverly A

Sep 20, 2016

When it comes down to it, there's simply not the support to assist a student that has a really hard problem, "hacker mentality" seems to equate to "figure it out on your own cuz nobody's going to help you". If things do not work perfectly for you then you are likely never to be able to finish because your "peers" don't know any better either. The way this class is set up makes me angry every time I have to deal with it. I would probably be just as well served doing just the swirl() exercises. I would quit if I hadn't paid all the way through in advance. I can't believe this is the type of school John Hopkins is to produce a course of this quality, but I guess I have to.

por Sergey K

May 10, 2016

This course mostly about how to use plotting libraries in R.

por Jean-Philippe M

Jun 16, 2019

More practical exercises using ggplot2 and clustering would be beneficial. Maybe need to be a 8 weeks module.

por William B B

Jun 12, 2019

Excellent course and applicable in my work right now

por Santi M

Jun 12, 2019

Good course

por Jamie R

Jun 07, 2019

Just an extended course on using R. There was little strategies for Exploratory Data Analysis, infact the example jumped from a high level view of the data to then start looking at individual counties. There are multiple tools in the market that will deliver in a better and faster way for exploratory data analysis. This course should be more targeted at developing a skill set that is tool agnostic.

por Eric J S

May 29, 2019

Best of your courses yet. Doesn't suffer from difficulty spikes when you hit the projects.

por Jorge B S

May 28, 2019

Very useful course with interesting contents.

por Nino P

May 24, 2019

Amazing! Learing so much how to explore the data for the first time. This is a must do for anyone who wants to be a data scientist. Now I can use ggplot without any trouble. Thanks!

por Joe D

May 19, 2019

Some of the links in the lectures are out of date, the forums usually have an updated link though.

por Piyush D

May 15, 2019

Awesome course ! It reaches you the crux of exploration of data . Although the SVD section could have been more thorough and detailed.


May 13, 2019

great cousre

por Patrícia A F

Apr 30, 2019

Great course! I'm learning a lot with this course, i recommend.

por Lidiya G N

Apr 29, 2019

Absolutely No technical help, like insane amounts of homework for each week. People have jobs and businesses to run. Incredibly short duration. Like literally this should have been spread out several more weeks. I would have dropped the class but I can’t. It’s so difficult to get i to the first set of practice assignments and these several sets. Honestly, I am literally getting no help on it and probably won’t pass because I am missing the deadline. I finished 5 coursera courses working on them for 24 none stop. I’ve literally been at this class all day. Besides all the insane amounts of assignments there’s tons of videos to watch and uploads to do. Go buy some books or take another class unless you are unemployed or have nothing better to do.

por David S

Apr 22, 2019

Great Course. Lectures did diverge from Quizes and projects but still was good practice of looking at a set of data and reporting out from it.

por Shreya S

Apr 16, 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.