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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

4.6
звезд
Оценки: 32,694
Рецензии: 6,976

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:

6276–6300 из 6,865 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Christian S

7 мая 2016 г.

I think this course could be integrated into the other ones of the specialization, or, if it is meant to be just a course to get an overview, be free of charge.

автор: Biplab G

3 июня 2017 г.

Helps to get the initial environment setup for the Data Science specialization.

Certificate received after completing the course is not effective/useful at all.

автор: Dherbey C

24 апр. 2020 г.

No subtitle in French as announced

It would be great to have the power point bigger when reading

The guide for connecting Rstudio and GitHub needs to be updated

автор: Islam D

22 февр. 2017 г.

it could have been better if it was more hands-on learning, for instance I don't understand why did we learn CLI till now and how will I link it to my studies

автор: Madalyn Z

4 мая 2016 г.

Might be a good introduction for those completely new to computational tools, but not useful for those with any background in git or R. Can be safely skipped.

автор: Quentin D

17 февр. 2016 г.

Good course about getting the basics for the Data Science specialization, but a bit overpriced, as the content is low, and can easily be done in 4-5 hours.

автор: Julian C

22 янв. 2016 г.

You don't really learn all that much, but then again I have experience with R and some data stuff already, so perhaps it'd be more useful for someone else.

автор: Farshad A

12 нояб. 2016 г.

It was a great start to data science but also students should have it in mind that the material presented in the course won't be enough to get through it.

автор: Stefan H

7 мар. 2019 г.

I understand the text to voice automation was done due to cost reasons, but listening to the automated voice is VERY exhausting! Otherwise great content.

автор: marcelo G

14 авг. 2016 г.

A very basic overview on Data Science. You learn how to use git, rstudio, and other tools though. The other courses of the specialization are way better.

автор: Ayush J

10 февр. 2016 г.

This course should be a free trial for whole specialisation. IT will be more helpful for students to know what is further stored in the specialisation.

автор: Woszczyk H

20 июня 2019 г.

If you already know your way around git and basic programming this is not a very interesting course.

I feel it should be included in the specialization.

автор: Peggy C

13 мар. 2017 г.

The word 'toolbox' made me think there was more in the course. 'Introduction' or maybe' Overview ' may have been more accurate. Good course otherwise.

автор: beth l

8 июня 2016 г.

I was hoping to learn more stuff I didn't already know. This class is more of just a vague overview of the other courses. Can be completed in 1 week.

автор: Jarod T

25 нояб. 2017 г.

Its was pretty good. I'm not really sure how important it is to learn Git so soon but it must be used in the next classes so I am excited to find out.

автор: Raven W

15 апр. 2016 г.

A good introduction to the course. Opening up quizzes to help feedback what we'd learned (for free learners) would have made the course much better!

автор: lcy9086

28 авг. 2018 г.

Everything is fine

I think they had better not include the GitHub thing in it without clear explanation.

It takes me too much time on that assignment

автор: Andy C

20 нояб. 2016 г.

Not much of a course, I understand why it exists, but it's basically just getting setup with the environment. Almost not worthy of course status.

автор: Milad

28 мар. 2016 г.

it gives you the necessary tools and knowledge for just beginning the data mining course. so you cannot expect to learn about the process itself.

автор: Sahitesh R

17 апр. 2018 г.

Less Content, should be more technical. Mostly repetitive from the the crash course in data science. Should have put an optional videos for git.

автор: SHREYAS A P

1 мая 2020 г.

THE COURSE IS GREAT BUT SOMETIMES IT IS HARD TO UNDERSTAND CERTAIN THINGS AS THE LEVEL OF UNDERSTANDING FOR SOME CONCEPTS IS NOT UP TO THE MARK

автор: Yu T K

29 сент. 2020 г.

I think this course has too many theory, I think it should contain more practical example for us to try....and too wordy

But overall it is fine

автор: Deleted A

12 дек. 2017 г.

Too much material. Too soon. I am new to R and the stuff was a bit overwhelming. The course got easier as I advanced through the other courses

автор: Bonnie M

28 янв. 2016 г.

The content is very basic. The whole course took my around 6 hours to finish. I think the instructor should add more solid training on GitHub.

автор: Rafaela C S

5 авг. 2020 г.

Estudar com essa inteligência artificial falando é IMPOSSÍVEL. E o material escrito só está disponível em inglês. Isso desvalorizou o curso.