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The Data Scientist’s Toolbox に戻る

ジョンズ・ホプキンズ大学(Johns Hopkins University) による The Data Scientist’s Toolbox の受講者のレビューおよびフィードバック

4.6
27,577件の評価
5,801件のレビュー

コースについて

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....
ハイライト
Foundational tools
(243件のレビュー)
Introductory course
(1056件のレビュー)

人気のレビュー

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.

フィルター:

The Data Scientist’s Toolbox: 3751 - 3775 / 5,671 レビュー

by Agustin A

Nov 12, 2018

Estoy bastante satisfecho con lo aprendido en este curso inicial del programa Data Science ya que es una buena introducción a todo lo que se verá más adelante. Debo decir como profesional de IT que me ha sorprendido cómo empieza desde cero explicando todo lo necesario para entender e instalar las herramientas informáticas necesarias para el curso con un nivel casi de principiante. Sin embargo en lo relacionado con métodos estadísticos y de análisis de datos el nivel no es tan bajo y los videos de la semana 3 han profundizado ya en algunos conceptos del análisis de datos. Espero que en los siguientes cursos se expliquen detalladamente desde cero.

by Nikolay B

Jun 17, 2019

Overall an interesting program is offered. Just started, an update is expected towards the end of the course. So far found an issue w/ quiz #1 (incorrect grading due a broken internal logic (?) where 2 different 'correct' answers are offered during subsequent quiz sessions). Also, I would say that the intro videos are too short to be useful. Anticipated scope is well aligned w/ modern trends that are re-branded from the underlying concepts known for a long time; such concepts were always being in the arsenal of any serious practicing engineer or scientist. Modern packages though are a nice compact up-to-date tools collection.

by Gurpreet S

Sep 05, 2016

I would recommend it to any one. The introductory course is so basic that some might see it not important but the course has done a well job by easily getting across the foundation of Data Science as well as helping non-programmers to easily drift into this field. I would have given 5 Star if i was allowed to attempt my tests even though i am auditing the course. The only thing coursera should benefit from is providing the certificate. By freedom of giving test and doing courses people will surely pay for one course or another when they get more confident with their results in audited course.

by Brendan S

Jan 24, 2020

Solid starting class that highlights the fundamental software you will be working with for the Data Science Specialization. It holds your hand at the beginning, but familiarizing yourself with the software may lead to a few bumps in the road.

Some of the issues were due to unclear directions and, at one point, a needed additional package to knit PDFs from RStudio R Markdowns. While the forums are not very active, there are a few people who might be able to help you. Also, Google (along with the listed Data Science forums) is your friend when looking for answers.

by Paras B

Mar 09, 2018

The course was really constructive. However, for the students who are really new to coding, courses where creation of git hub account or coding to push/pull data from git hub is involved, i would suggest to add more videos related to step by step coding. Also, there are some irrelevant questions involved in the weekly quiz which are not very fruitful when it comes to learning this course. I would suggest these questions to be removed. Team can contact me on my email Id if they require complete feedback for such questions.

Hence I would rate this course 4/5.

by Marc F

Feb 29, 2016

I found this course a fairly easy introduction to the tools you will need for this series of courses, however I already had a rudimentary knowledge of Linux and Bash Shells. For the computer novice this may be more daunting. The one area that is worth spending some time on as an investment for the future is git and git-hub. Understanding how these work together is not transparent. It took me a while to figure out what I had to do to push committed files to a remote site. I think suggested reading could be more specific to guide people in this area.

by Marloes d M

May 18, 2020

The course was well structured and I compare him with two other beginner courses for data science. So far, the best one. I prefer following my own pace and opt for either video or script. The audio voice was a bit monotone but otherwise ok. I liked that there was some attention to data analysis background at University level but it was pretty basic. The final project was good since I had to redo it a couple of times before I submitted and those skills are now pretty permanent I guess:) Thanks for the clear (most of the times) guidelines.

by Ariel M

Feb 06, 2016

The Data Scientist’s Toolbox is a great way to dip into Data Science and the methodology behind it. The course is very general, and makes an effort to cover the bigger scope of things without delving deep in any. More than anything, it's a great way to learn the components and uses of data science and set a framework for all that will be coming after.

The materials are very well laid-out and almost feel like attending college classes. The visuals and slides are a little dry, but the pace is lively enough to maintain momentum at all times.

by Francisco J D d S F G

Aug 28, 2016

A light introduction to the Data Science field, in many ways it can be difficult for inexperienced people with software or inexperienced with stats - in my case it was not very difficult since some of the topics were already familiar.

The course can be done in a couple of days if the topics are already familiar, in my opinion the course's contents are perfect for someone very new to this field.

I would have rated more stars if the course's content was more "objective" for people unfamiliar with the subject - other than that it's perfect.

by Hathairat W

Dec 01, 2018

I got some bugs when running git bash and I had no clue how to fix them. I kept watching videos over and over but I couldn't find the answer. Then I tried google, reading many websites and doing trial and errors until the errors were gone. I understand in real life this is what I need to do but it would be good to know some proper ways of fixing errors after I submitted the assignment. So I can learn and use in future! Apart from that, the course is really useful and prepares me for the next stage.

by Erli L

Jun 03, 2017

It is a very good introductory course for anyone who would like to learn data science, or would like to use tools in data science for their everyday work (like me). In the course you will have some general essential ideas about what is data sciences and which tools are used in the science, and the most importantly, the concept of version control and the GitHub tool for the purpose. However, there will not be any in-depth knowledge in the course, which is determined by the introductory nature of it.

by Carolina B

Aug 06, 2020

Es un muy buen curso teórico práctico sobre la introducción a la programación en R. Sin embargo algo que me desmotiva mucho es que las constancias no proporciones ningún crédito. Éste curso tiene una duración de 4 semanas y no me parece justo que no se tome en cuenta si se acreditó correctamente no tenga ningún valor. es el segundo curso que tomo en coursera y me pasa lo mismo, dos universidades renombradas que no proporcionan ningún crédito. ¿Cuál es el objetivo de ofertarlo entonces?

by Patricia L A

Sep 01, 2018

On enrolling in this course I knew nothing of the data science world and always wondered how all that "jumble" of data was organized. After this short course I am beginning to have a glimmer of how this is done. I know there is more to learn and I am curious to know how. I must say that I struggled quit bit towards the end of the course with the assignment, but I believe as I continue with the other courses I would become more proficient using RStudio and GitHub, etc.

by Akshit M

Jul 07, 2016

Important note: Opt for this course only if you plan to do the entire specialisation. It is developed solely for the specialisation and not as a standalone course. You will not learn much concepts or theories or practice any R programming here.

In general this course was basic and good enough to get someone started for specialisation. The video lectures for setting up R, RStudio and Github account were helpful and very basic ( maybe coz I already had an github account).

by Howard G

Jul 28, 2020

A lot easier than I expected. In particular, compared with courses where one final project took longer than the rest put together, the project is just to put together your R/Rstudio/Github environment. There's a lot of value in that; plenty of courses you learn the concepts but don't add anything to your repertoire; if a year from now I'm putting a little project on my new Github site now and then, that will have more actual impact than a lot of harder ones.

by Jonathan S

Aug 09, 2017

I wish this course would spend a little more time upfront saying at a very basic level what data science is and gave some real life examples of data science in action. Most of the course is configuring software that you will be using down the road, but it would help to know why you'd even want to use the programs and in general, what their capabilities are before you get into setting them up. I imagine that latter course will do this (at least I am hoping).

by 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

by 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.

by 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.

by 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.

by 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.

by 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.

by 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.

by 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

by 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.