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

4.6
étoiles
29,302 évaluations
6,234 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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126 - 150 sur 6,103 Avis pour Les outils du scientifique des données

par Atul

Oct 21, 2020

As I want to become a Data scientist and I enrolled in this course this course is amazing teaches allot of thing. As when I enrolled in this course they taught me from the basics. In my past 2-3 years I was not knowing about Rstudio, R-programming etc. But coursera taught everything from basic. Now I fell that I have enrolled in the right course

par Igors K

Mar 12, 2019

good tool tips but with some hard to make SSH and pull not working downloaded github desktop and take projects from RStudio and pull, using github desktop maybe add some tips for this program because to some person it will help like me, every have diferent PC and language.(Searched google every writing about some space not about solving problem)

par chitradeep g

May 16, 2017

I would like to thank Coursera team for providing such a good opportunity specifically the mentors of JHU, for sharing excellent video lectures. I would recommend my friends, colleagues, and juniors about this course, who is having keen interest to grow into the field of data science. Looking forward to complete the upcoming series of sessions.

par Vanessa K

Sep 09, 2020

This was a great beginner course given that I had no previous experience with coding or R Programming. It went at a good pace, I could work on my own time, and I appreciated the option for both video and script. While it only taught basics, it provided websites and connections needed to continue my understanding of the data science community.

par Rolands Š

Apr 18, 2020

Course is not as time-intensive, however it contains a lot of important information and helps lay out solid foundation in data science. Information is presented in an easily digestible and engaging manner, including some fun references/easter eggs that help lighten the mood. Tutorials for software are also easy to follow. Highly recommended.

par Theresa B

Aug 12, 2018

I really loved the presentation style. It cratered to all learning styles which I find to be essential for online coursework. The information was basic, but it never hurts to have a solid foundation before going to the next level. Since the next course really throws learners into the deep-end, this course is necessary to be ready to go.

par MASROAF S S

Sep 04, 2020

This course is a kickstart to the further R-programming related courses. Those who want to learn R programming with strong basic on its background, then this course is the right choice. The course materials are too much easy to complete but easy to those who attentively listen to the lecture videos. Heading towards the next course now!

par Pavel T

May 15, 2017

That was introduction to Data Science specialization. Not too valuable as independent cource, but basic for whole specialization. Speaker briefly informs us about purposes and specific of data scientist job, indicates common mistakes and review tools for data analisys. Narrator is pleasant, seems like he is professional in this field.

par Scott C

Apr 05, 2016

This course is a good, brief introduction to the foundational concepts of data science and some of the tools you can expect to use when doing data analysis with the R programming language. It's best suited to someone who intends to continue at least with the R Programming course that is also a part of this Data Science Specialization.

par Randal N

Jan 02, 2018

Great introduction to data science and the associated tools. As someone new to data science this course provided a simple, yet firm and comprehensive foundation for the rest of the courses in the data science specialization. Definitely worth doing this course if you are thinking of pursuing any endeavors in the field of data science.

par Jonas H C W

Aug 21, 2020

I thought this was a very fun experience. It was easy to follow along and it didn't take up too much of my time to pick up some interesting concepts. Though I do recommend that you watch some videos on YouTube with explanations on Git and GitHub, because that will really help make version control systems so much more understandable!

par Yanal K

Jan 07, 2016

First experience got me hooked. I love coursera. This course, even though an introduction taught me a lot and showed me an error of my ways in everyday life. One question in the 3rd Quiz was very confusing to answer. But that's about it. I hope the rest of the specialization carries on forward in a similar maybe even better pattern.

par Sandhya M

Dec 29, 2019

The course was completely new to me. But the step by step instructiona made it easy for me to complete the course succefully. Linking of R and GitHub is like a magic to me ,who is new to programming. The Video on Control Version is fantastic and can be helpful for even professors like me who give group projects to their students.

par xi l

Nov 20, 2017

I think this course did gave me a full impression regarding data science and guided me how to install basic tools successfully. Also, I found that the help guide and links those are introduced during lecture are very helpful, such as "Github help" and discussion forum. I used them a lot when finishing the last peer view project.

par Daryl B

Jul 31, 2020

This was an excellent course. I always wanted to know how to use GitHub and this course knocked it out of the park for that. Also learned how to set up R & Rstudio on Debian 10 vs. Windows and/or Mac. I'm a FOSS guy so this course provided sufficient guidance around how to build my Data Scientist toolbox on that platform. Nice!

par Egor M

Jul 23, 2020

A short introduction to the primary tools and concepts for data analysis with R. The lectures cover a fair range of topics from installing the software to experiment design and types of data analysis. Relevant and informative examples are provided for each section. All in all, the course is a wonderful introductory experience.

par Selwyn L

May 01, 2018

I liked this course. The material that you learn was pretty basic, but the community helped you digest the more indepth concepts (I am new to RStudio and GitHub). What some people will find as a flaw, I liked the fact that I had to go and look for the answers online and it wasn't just given to me in the course notes/slides.

par Mainza H

Oct 30, 2017

This course has really helped me understand how to value peoples work and share what i think and share my work with others. The lectures are very good and helpful. From the knowledge i have arquired i view things differently, more like a data scientist and i am motivated to complete my specialisation as a data scientist.

par Nanette H

Feb 16, 2016

Great course! Explains the details of what is in the content of the rest of the certificate while not being too detailed. Showed which classes will be related to the content of each part of the introduction. Did a great job of setting up all required tools for the the R programming course (which I am currently taking).

par Haripriya R

Jul 03, 2020

I loved the course - the different kinds of formats of presenting the information really helped me in choosing the medium in which I wanted to learn! Thank you to all instructors and the links to all the material provided. I loved Hilary's name analysis and Nate Silver's take on US elections - thanks so much once again!

par Ayush R

Apr 02, 2020

before completing the course I thought that I know all of its content, but as I proceed further I realized I was wrong and got to learn many thing, the most fruitful thing that I learned here was integrating Git and R Studio, the final assessment practically brushes up all the learning taught. overall am very satisfied.

par Kiel A

Mar 20, 2016

Really solid introduction to the subject matter. This course gave me a better understanding of how to go about finding the questions which need to be answered, which is fundemental to the study of data science. Also, it gave me a wonderful tutorial on where to find help and how to ask for help which I found very useful.

par Raksha S

Mar 24, 2020

This course is just amazing! I had the best experience learning it at my own pace, and the interactive learning session was an added fun to the whole course! I am looking forward to learning so much more! The slides had very illustrious graphs and teaching was in such simple words that I could grasp everything so well!

par Shannay R

Mar 03, 2020

An excellent introduction to R, RStudio, Git and GitHub. The course content is well designed and touches upon various topics in a crisp tone. The course lowers inhibitions and allows learners without any coding experience to test the waters.

Definitely recommended for all enthusiasts looking to venture in Data Science

par Jose P M L

Jul 09, 2020

Very nice introduction to the subject. If you know RStudio and Git you can easily go throught it in a couple of days. You might really wanna go deep into R Studio features like markdown syntax and take a good look at Hilary Parker's work to see how a data scientist think and go through her process. Her blog is amazing