Chevron Left
Вернуться к Набор инструментальных средств для специалистов по обработке данных

Отзывы учащихся о курсе Набор инструментальных средств для специалистов по обработке данных от партнера Университет Джонса Хопкинса

4.6
звезд
Оценки: 32,637
Рецензии: 6,964

О курсе

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....
Основные моменты
Foundational tools
(рецензий: 243)
Introductory course
(рецензий: 1056)

Лучшие рецензии

LR
7 сент. 2017 г.

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 апр. 2020 г.

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.

Фильтр по:

6326–6350 из 6,853 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Lucas P

4 апр. 2016 г.

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

автор: Khoa N

24 окт. 2016 г.

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

автор: Morgan D

18 апр. 2016 г.

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

автор: Aditya K R

3 мар. 2019 г.

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

автор: Ahmad A

7 окт. 2018 г.

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

автор: Rafael T M

7 апр. 2019 г.

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

автор: sérgio C

16 янв. 2017 г.

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

автор: Deleted A

29 дек. 2016 г.

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

автор: PATHIK C

13 окт. 2016 г.

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

автор: Andrey T

6 сент. 2016 г.

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

автор: Tamir L

25 июля 2016 г.

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

автор: James M

24 февр. 2016 г.

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

автор: Aneesh B

26 мая 2020 г.

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

автор: Aritra D

24 июля 2019 г.

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

автор: Leyla C

24 февр. 2020 г.

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

автор: Roman K

10 окт. 2017 г.

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

автор: Bill S

12 апр. 2017 г.

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

автор: Shawn O

28 мая 2016 г.

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

автор: Shrestha P

18 янв. 2020 г.

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

автор: Jensen K

7 февр. 2016 г.

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

автор: Simin X

5 мая 2017 г.

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

автор: JOHN J O G

28 окт. 2016 г.

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

автор: Kris

20 дек. 2020 г.

Not a fan of the quizzes since they do not provide rationales or solutions if you got an answer wrong.

автор: Abdallh A

11 дек. 2019 г.

Good course for supporting the rest of the specialization. However, it has very little use on its own.

автор: M S G

3 февр. 2019 г.

Would have loved it even more if there was an in-depth explanation of how to use GitHub using Git bash