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

Оценки: 29,285
Рецензии: 6,230

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


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.


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.

Фильтр по:

4701–4725 из 6,100 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Suzanne H

Dec 26, 2018

Good introductory course, requires other courses to really get your teeth into R

автор: Kumaravelu N

Aug 12, 2018

Unable to continue with my next chapter though i have completed my previous one.

автор: Harraj S S

Jul 13, 2017

Very nicely presented... easy for a newbie like me too.

Thank you for the effort.

автор: Debanshu K

May 27, 2017

This was superb in order to create a basic understanding regarding data science.

автор: William I

Sep 26, 2016

A solid course to kick off the specialization, but not worth taking on it's own.

автор: Boris B

Feb 13, 2016

I would expect some more methodological material,\though everything else is fine

автор: Natalie

Sep 15, 2020

Tuve problemas con Markdown, pero todo lo demás lo pude solucionar directamente

автор: Abdou k a

Apr 26, 2020

The Course is very Good but requires more details during the practical examples

автор: Albert P

Jul 23, 2019

It's fairly basic. Completed the whole course in 1 sitting in about 4-5 hours.

автор: Chenjun L

Apr 20, 2018

It is a good start with good background info of resources in data science field

автор: Rojbin L

Nov 23, 2017

More practical exercises please! Thank you for offering such a valuable course.

автор: ying2017

Jul 28, 2017

good course somehow a little hard for those who are not native english speakers

автор: Adya M

Apr 17, 2017

Elaborate introduction to the data science arsenal of tools. Thank you so much.

автор: Oskar L

Apr 10, 2017

Good introduction to the Data Science and the courses provided here in Coursera


Sep 01, 2020

Overall course is good but please decrease the communication speed of lecture.

автор: Jinghui Z

Dec 09, 2019

Course can go quicker, the content is useful but it is too little for one week

автор: Jahnavi T V

Aug 21, 2019

I really feel the entire course is explained so thoroughly for the beginners .

автор: Manuel R R

Jan 26, 2018

Decent introductory course to Data Science and setting up the necessary tools.

автор: Kevin C A

Jul 26, 2017

Buen curso introductorio. Le falta quizás profundizar sobre algunas funciones.

автор: xun y

May 29, 2017

great course, hope professors talks more about github command line operations.

автор: Josh W

Jan 31, 2017

Very basic. I am hoping it helps guide my time commitment for further classes.

автор: Dennis J S

Nov 08, 2018

Some of the closed captioning on the videos does not match up with the audio.

автор: Chris H

Jul 10, 2018

Easy to follow. Practical and useful tools. Lots of good reference materials.

автор: Ryan H

Aug 21, 2017

Great intro. Pretty basic if you are a programmer but overall worth the time.

автор: Rafael C

Feb 26, 2017

It would be better if more details included in the lecture on Git and Github.