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

4.6
étoiles
28,853 évaluations
6,119 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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4001 - 4025 sur 5,980 Avis pour Les outils du scientifique des données

par Jason D

Apr 08, 2020

I would have preferred more hands-on examples or projects for each week's lessons. As an entry-level practioner, this course felt thin. There is a lot to absorb and while my interest and curiosity is peaked, I've found I've been able to grasp a better understanding of the material outside the classroom rather than inside. This is ultimately a good thing but I would have liked some more "hand-holding" from the course to feel more comfortable moving forward

par Prottay H

Feb 03, 2020

Great intro course. Got me in the mood, established the perspective I should have going in. I felt that some of the lessons like R Markdown were a bit rushed and at times I felt like I was just following along without understanding the commands and ideas. Perhaps that is simply a lack of emphasis or a lenience in tone (like don't worry, we'll look at this later), but in that case I suppose it did not translate through the synthesized speech.

par Saquib C

Sep 01, 2020

Although the modules were supposed to teach us how to setup RStudio and git on our computer, I found that they ignored a lot of common errors. I use a Mac and had to spend a lot of time on the web looking for answers to how to complete the setup. Although the solutions were pretty straightforward, it took me a long time to identify and pick the right solutions, the right downloads and the right tweaks to get everything going.

par Dariusz S

Nov 12, 2017

It's a very introductory course where topics are only mildly touched upon but I guess this is the goal of it. Git, GitHub, R, RStudio, Statistics... these are only signalized and very basic introduction is given. I trust that the other courses that constitute the whole Data Science Specialization series will dive deeper into the individual subjects. But as an introductory course The Data Scientist's Toolbox is OK.

par Luis F d R X

Jun 24, 2017

This is an introductory course to the vast theme of Data Science. Fundamental concepts in data science are given and also the access to the most commonly used tools is showned, as its name suggests. You'll learn which questions to ask and how to answer them. You will setup your data science lab in your pc (R Studio) and join the development community using GitHub. An entry-level well paced intro course. Very Nice.

par Abhijna R

Jan 06, 2017

The narration and content are excellent. The clarity of the slides has given neat direction steps to installing the software. However, I was not able to co relate the Command Line Interface with the R console after installation. I was overwhelmed with the huge list of commands immediately after software installation. The Week 3 videos have great information but lacks a coherence with the remaining course content.

par Harrison K

May 27, 2017

This course was a very good broad overview of what data science is. I've taken some courses tangential to the topic before, so it wasn't particularly groundbreaking. I encountered some complications installing software and didn't feel like it was always very clear what order I was supposed to do things in, and I wished I'd had more help since installation issues can crop up much later and be hard to diagnose.

par William H

May 19, 2020

The text to voice simulation needs work. In particular, it does not understand that when the word "record" is used as a noun, the accent is on the first syllable. When it is used as a verb, the accent is on the second. Also, to output markdown files to pdf, on Windows machines, one needs MikTex and to link it to R Studio. I have downloaded MikTex, but I have not yet figured out how to link it to R Studio.

par Benjamas T

Jun 04, 2020

The content of the course is very detailed, including a step-by-step guide which really supports beginner as the course promoted. The pace of the course is just right. The only comment here is that, while I understand the underlying reason, the course is presented using a text-to-speech voice which makes the course sleep inducing. Overall, it is a good starting point for those who want to learn R without a

par Ying T

Jun 04, 2017

I like the structure of this course, it introduces essential tools for people who just begin the journey of becoming a programmer. But the quality of videos needs to be improved, especially the instructor is basically reading the slides and sometimes it's distractive and boring... Besides, I don't think it's necessary to include all the introduction sessions for other courses in this specialization.

par James J

Dec 26, 2016

Are you ever tired of long-winded professors, focusing on a lecture for an hour or more, or spending hundreds of dollars for a class? Well, this course gives you the structure of a course, but the video lectures are concise, the topic is to get you in the door for data science, and all of my questions were answered quickly. I recommend this course to anyone who wants to learn data science.

par Yean D

May 25, 2020

Great Course. But for completely beginner who don't have any previous experience in programming or have some experience in other language but not in R Programming Language, might find it difficult to cope with. Many of the times, I had to google and watch other tutorials in youtube to understand some lessons of the course. Besides the artificial robotic voice was quite irritating for me.

par Danish N T

Jun 19, 2020

I get that this course is more catered towards people who have never set u Softwares like Matlab and Rstudio and have never utilized Git but I felt that if you are touching these topics, it would be better if it were more in-depth and made a strong foundation. The statistics part of the course was excellent but I felt more attention could be paid towards Git and Bash and CLI in general.

par Victor K

Nov 07, 2018

Good introduction to the Data Science Toolbox. I found the course very engaging, setting up foundation for further studies. Also, very nice platform supporting In my mind though taking this course makes sense only in conjunction with the other courses within this Data Science Specialisation as knowledge you get from Data Science Toolbox will not be sufficient for practical application.

par Oleksii P

Nov 22, 2016

This is my first course on Coursera so i didn't really know what to expect and i apologise in advance for my ignorance. The course only takes you 8-10 hours or so to complete (even accounting for reading external sources) spread across a month. I enjoyed it, but it doesn't feel like i learned a lot and i think it could be a bit more intensive. Looking forward to R Programming (part 2)

par Martin J M

Mar 11, 2017

With my background, this was a lot of repetition of information that I already knew, but I was happy to have the reminder and it would be an excellent introduction to the specialization for others who do not have as much of a data science/computer science background. Overall, I feel that the course was worth my time and I feel it is a great start to the Data Science Specialization.

par Hong C

Jun 29, 2020

It is really a long journey to me ( 6 months) and the study itself has ups and downs. The first few courses and last few course are relatively easy to me, but the statistics reference and regression model are really hard, and Capstone is the most difficult and time consuming course/project ever. I am glad it is all over and pretty sure it will be worth the time and effort on it.

par Jen V

Oct 08, 2019

I found it somewhat difficult to figure out how to push the file to GitHub (luckily I did find some helpful info in the forums), but otherwise found most of this course very easy. I can't figure out how to download the slides in such a way that allows me to click on hyperlinks, and I've seen other people say the same thing. I also would recommend making the code & graphs bigger.

par Keuntae K

Sep 11, 2017

Overall, a great course. However, I hope that the course will focus on explaining overview and principles of data science, not explaining how to use toolboxes such as R and Github. R and Github as data science toolboxes are not well explained in this course because it was quite difficult for me to follow what the instructor showed in his slides about using codes in R or Github.

par Маношин А А

Mar 20, 2020

This is a good course. You will recrive knowledge that was promised, but 1 mounth for this is too long. 2 weeks is long for this too, but at least appropriate. Also I have encountered some problems in the process that were not mentioned in course. It was not too hard to solve them but this is it.

Thank you for your course! I will definitely take the next course in the program.

par Rick M

Sep 12, 2020

Enjoyed the course and learned a lot overall. It was the kind of intro and overview of data science I was looking for. Several of the quizzes need a review of the answers in the multiple choice questions. As my first class taken in Coursera I found having to retake a quiz and game it to get the "correct" answer that was not actually the accurate answer a little frustrating.

par Jose A C

Nov 30, 2018

I think the course is very good. I believe that it could be improved if the videos could reflect a more realistic approach on how to get to certain sites and how to download programs. Even though the students get the final result, in some cases it is frustrating and time consuming to determine if what we are doing is correct. In general, I am very satisfied with the course.

par Abir N

Jul 10, 2020

This course is about nuts and bolts of R software, Rstudio interface, Git and Github with a brief inroduction to version control and other prerequisite of building a data science project. The only downside of the course is it uses automated videos which is a bit mechanical sounding.Though it is my personal opinion but that's the the cause of a missing star in my review.

par Dylan B

Jul 08, 2020

All of the course content is excellent however there are issues with the peer review system. The final project is peer reviewed by only 2 people (per submission) so the mark has a very high amount of variance and so is essentially luck based, which means you have to submit many times until you get a pass mark on it. Other than this there are no problems with the course.

par neil v a

Nov 14, 2017

It's all fine and well to just learn and do things by watching youTube and reading internet websites.

But your learning needs some structure, because at the beginning of the process, you don't know what is important and what is not ! I found the course good at helping give me an oversight to the subject, and getting me up and going with the software components needed.