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Avis et commentaires pour d'étudiants pour Les outils du scientifique des données par Université Johns-Hopkins

4.6
étoiles
28,708 évaluations
6,074 avis

À propos du cours

In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio....
Points forts
Foundational tools
(243 avis)
Introductory course
(1056 avis)

Meilleurs avis

SF

Apr 15, 2020

As a business student from Bangladesh who is aspiring to be a data analyst in near future, I love this course very much. The quizzes and assessments were the places to check how much I exactly learnt.

LR

Sep 08, 2017

It was really insightful, coming from knowing almost nothing about statistics or experimental design, it was easy to understand while not feeling shallow. Just the right amount of information density.

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4051 - 4075 sur 5,959 Avis pour Les outils du scientifique des données

par Marco L

Feb 05, 2017

It was a little to easy and the quizzes were not really necessary. Questions like "What courses are in the Data-Science Specialization?" don't help to controll my learning progress. However for a first, introducing course it was okay. R Programming is way more interesting and challenging <3

par Ziaur R

Dec 20, 2019

Didnt enjoy the voice on the automated videos, but was faster at reading than watching videos. The document didnt work for the Big data Section and had to watch the video for this. Good introduction and wished I had more questions to practice! Looking forward to R Programming section next"

par Glauco G d A

Jan 11, 2018

It's a good start point for people who wants to start pursuing a data science career and haven't a statistical background. Explain the basic definitions of research analysis types and shows the very beginning of handful tools like how a git repository works and good editors for R scripts.

par Marek B

Mar 11, 2018

The course is very basic but still contains useful information both on data science and some of the tools.

Unfortunately, because of how basic it is, I found the quizes focusing on trivial and subjective questions that are both hard to answer and not really testing any interesting skills.

par Candice J

Jan 24, 2020

The tools needed are all explained well, including installation. Still getting used to the new Amazon Polly format. A few questions in quizzes seem to not align with updated material, but that could just be an intentional push to be resourceful. Looking forward to the next course.

par Sarah G

Sep 06, 2017

Overall a really nice course for looking into Data Science. I would've liked more on the general field of what is data science and what kinds of problems you might solve, etc. But the lectures were good and the timing was very manageable for working professionals to do. Thank you!

par Alberto H A

May 19, 2016

I found this course to have very useful material and good, clear explanations. My only criticism is that the last of the four weeks has practically no content. There are no lectures and the only assignment is grading the assignments of other students, which at most takes 20 minutes.

par Lee K

Jun 29, 2020

The part on how GitHub works (Including the Git Bash) section could be further discussed for a better understanding of how to use the platform. Overall it's a good course! well structure. just that content could be more detailed so that it will be a even more meaningful course :)

par Figo C

Dec 04, 2017

Great learning on the basics of Data Science and it's importance in real-world applications. Help to get started with introduction to Python, R Language, Git!

Lectures could perhaps be more engaging and have more visual appeals (instead of having just lots of words on most slides)

par Guilherme B D J

Feb 16, 2016

This course is good to get all your programs set up before you start your studies in Data Science.

I think it could offer a little bit deeper knowledge of git and github in order to guarantee it will not be a problem later, since they will not be strictly related to data science.

par Eugenia G

Jan 22, 2016

The course content is very useful, but explanations are short and It's unclear how to install R studio for the Windows (I found it at Youtube). Also I had a problem how to install the R packages, and solution was simple: you should run it as administrator (it wasn't in lecture).

par Ximena L R

Mar 31, 2020

I felt like I was able to keep up with the course material fairly well. My only critique would be when it comes to using git, the commands aren't very intuitive to me. Maybe explaining the commands a bit more would be more helpful, i.e. what the commands are telling git to do.

par Rahul P

Jan 25, 2017

Very nice introduction! Unlike a lot of online courses, this course is no fluff or jargon. It is solid stuff with hands on experience. I only wished this course was longer. After completing the 10-week Machine Learning course by Andrew Ng, this course felt a bit too short. :-)

par Colin L

Mar 31, 2020

Very basic. A few tweaks are needed in the last quiz's questions - the one pertaining creation of a .md vs. a .rmd file, and how to make sure the "## " prefix is properly given. (There should be a space after, and graders need to look at the raw file, not the presented view.)

par Madhusudhan T

Mar 24, 2018

An interesting introduction to data science, Git and GitHub. Hope GitHub is explained in a little more detail. Quite a few people found a couple of problems with the final project. The community is great and there are people who will help. Looking forward to the next course!

par Tina L L

Apr 28, 2017

The course is great but there are some serious glitches happening in the Coursera platform that desperately need attention. I just went from showing that I did not pass the peer review section and in the next second was greeted by a big green Course Completion Certificate.

par anjali v

Apr 01, 2018

This course is a great introduction to what data science essentially is and all the necessary tools required to start your analysis. However, it would be great if the examples used in the videos were explained a bit more in context rather than being stated plainly.

Thanks!

par Zainul A

Dec 21, 2017

A little unclear about the process for using Git & Github. The common functions/code are thought, but I believe a demo or a video review for the last assignment should be shared. Other things in the course provide a good introductory insights to the world of Data Science.

par Tanmay B

Mar 23, 2017

It is a really nice course if you plan to complete all the 10 courses in the Data Science Specialization track. As a standalone, It is not that great a course as it basically introduces you to different things and you need to do other courses to actually learn something.

par William B B

Mar 07, 2019

This is an excellent basic course. The main problem I had was understanding the computer voice at times. There is also a quiz question or two that refer to commands in Studio that are not up to date, but only a couple that I found. All in all, it's an excellent course.

par Naveen K

Nov 27, 2016

Great intro to Data Science Specialization. Hoping to complete the other courses as well. Dispels my myth about Data Science is all geeky stuff. Looking forward to bust more myths.

This course is light, broad and introductory. 4 weeks is a sweet spot. Keeps you engaged.

par Apolline M

Oct 23, 2016

Not much to learn, I would have liked a more thorough introduction to data science's principles.

Yet, everything is really presented step by step to make sure that all participants install correctly all tools needed for the further classes included in the specialization.

par Tony D C

Apr 06, 2020

This course is perfect to get an introduction to R and RStudio and the Github. It's easy to follow and pretty fast to complete. Probably the best thing you take home from this is to have a nice setup for the following courses where you can use the tools presented here.

par Bernardo M F d S

Mar 09, 2018

Although I understand that Data Science involves a lot of self-oriented research, more resources and recommendations for learning git basics would be appreciated. Perhaps some practical exercises before the final assignment would've ensured a better learning process.

par Rok B

Apr 04, 2019

It is a good start to data science, you don't need a background in programming. The course is aimed at 1) helping you set up R, RStudio,git and conect it to GitHub and understand it's basic functionality and 2) getting a basic understanding of what data science is.