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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

Оценки: 29,942
Рецензии: 6,387

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

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.

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:

4851–4875 из 6,246 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Yuting Y

16 июля 2018 г.

give me a basic idea. course quite easy, but need to learn more to be useful

автор: Amelia L

14 сент. 2016 г.

Helpful introduction to people who have little experience with GitBash and R

автор: Joel M

15 июня 2020 г.

This is a really good course to begin with. I highly recommend this course.

автор: Kārlis L

22 авг. 2017 г.

Good basics, but 4 weeks is way too long for such small amount of new info.

автор: Michael M

14 авг. 2017 г.

Good class. Need to update the content though. Same from at least two years

автор: André G d L M

7 нояб. 2016 г.

Com o curso conseguir aprender muito e hoje posso colocar em meu dia a dia.

автор: Robert D

18 окт. 2016 г.

Good introductory course to get started on the Data Science Specialization.

автор: Thierry L B

8 авг. 2016 г.

Good and relatively easy course. Perfect to establish personal confidence.

автор: Layla M

26 июля 2020 г.

I've learned a lot taking this course and I look forward to learning more.

автор: Shivam K

23 сент. 2019 г.

course is pretty good but the accent used by instructer should be improved

автор: Zbigniew B

11 окт. 2017 г.

A little basic but a good introduction for the rest of the specialization.

автор: Souvik R

14 авг. 2017 г.

Needed. If nothing else at least to know what software we need to install.

автор: Natalie K

2 апр. 2017 г.

Very introductory - can finish the class well before the 4 weeks are over.

автор: Supriya P

17 окт. 2016 г.

Informative. Making it more elaborate in terms of coding would be helpful.

автор: Rafael B S

27 сент. 2016 г.

This is a basic course but since this is what it is design to be it is ok.

автор: Nishant k

19 дек. 2017 г.

Its a good starting point for Data Science. Expected more content though!

автор: Misbah A

22 июля 2017 г.

I have really Enjoyed this course and Really Happy to Get the certificate

автор: anand v

19 июня 2017 г.

Detail tutorial on basic of Data scientist tools and good start up course

автор: Yolene D

6 апр. 2017 г.

Great way to get started on the Data Science course with the right tools.

автор: Irakli N

11 июля 2016 г.

It was good, well-executed course, but it was too basic. I expected more.

автор: Abeduddin B

22 сент. 2020 г.

if live instructor leads the class would be much helpful than auto video

автор: Elizabeth K

10 июля 2020 г.

Some quiz questions are a little out of data but overall a great course!

автор: KESHAV P

18 апр. 2020 г.


автор: Deena S

9 дек. 2018 г.

more hands on exercises (not just in the quiz) for Git would be helpful.

автор: Guillaume M

2 мая 2018 г.

very comprehensive and simple introduction to the basics of data science