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

4.6
étoiles
33,150 évaluations

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

LR

7 sept. 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.

SF

14 avr. 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.

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6451 - 6475 sur 6,978 Avis pour Les outils du scientifique des données

par Christopher F

11 juin 2018

Content is great for an absolute beginner. For anyone on the intermediate end of the spectrum it will seem very basic.

par Jad A

13 nov. 2016

Quite basic - but good overview of general theory. Don't do this on its own, it's quite useless as a standalone module

par fares a

8 sept. 2017

its just an introduction to R please make it more explanation to R even if there course about it

and thanks very much

par Lucas P

4 avr. 2016

The course accomplished its goal. Nevertheless, since it is a paid course I expected to find more theoretical content

par Khoa N

24 oct. 2016

very helpful but i think it is too short, i need some more concepts to fully understand what these tools used for

par Morgan D

18 avr. 2016

Lots of very general review information, not much new stuff. Did force me to start getting familiar with gitHub.

par Aditya K R

3 mars 2019

Super basic and boring stuff. Mostly talks about R Studio and GIT. Not of much help to those with IT experience.

par Ahmad A

7 oct. 2018

the voice is too low and the level of instructing are not for beginners, but with a lot of effort you could pass

par Rafael T M

7 avr. 2019

Very introductive. I hoped more about this course. There are interesting stuff but I'd rather more work volume.

par sérgio C

16 janv. 2017

Github exposition is a little complicated. I believe it could be more detailed. But the rest is very well done!

par Deleted A

29 déc. 2016

Doing this course first doesn t make you anticipate how difficult could this speciality in the next courses :p

par PATHIK C

13 oct. 2016

There is practically nothing in this course to learn. It can be termed as a introduction to the specialization

par Andrey T

6 sept. 2016

The course is too easy to be called a course. Just an easy intro, which you can complete in a couple of days.

par Tamir L

25 juil. 2016

Very short, easy and introductory course. Gives a nice high altitude overview of the subject but little else.

par James M

24 févr. 2016

Not useful by itself, but grudgingly necessary to get you ready for subsequent courses in the specialization.

par Aneesh B

26 mai 2020

It is good for those who are looking what is data science and how to install R and Github and how they work.

par Aritra D

24 juil. 2019

A little more depth on R and R studio and the rest and more data driven projects would have been appreciated

par Leyla C

24 févr. 2020

Easy introduction into R, maybe a bit too slow though. Course name is a bit misleading and not informative.

par Roman K

10 oct. 2017

The first 2 lectures (overview/introduction) are pretty good. The tools/practical material is very trivial.

par Bill S

12 avr. 2017

Mostly preparatory material and setup activities for the rest of the series. It's OK, but not a revelation.

par Shawn O

28 mai 2016

Could be completed in a single day but spread across 4 weeks. I could understand a week but 4 seems silly.

par Shrestha P

18 janv. 2020

It was great to know new terms and tools used in data science. However, the course is mostly theoretical.

par Jensen K

7 févr. 2016

Needed a step-by-step information sheet about what R Software and Tools need to be downloaded with links.

par Simin X

5 mai 2017

It's only enter-level for people who don't know R. For those who already used R, it's not a good choice.

par JOHN J O G

28 oct. 2016

Buen tema y contenido pero muy resumido o simplificado, se debería ampliar mas la 4 semana como mínimo.