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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: 31,502
Рецензии: 6,696

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

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.

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.

Фильтр по:

4726–4750 из 6,552 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Wai M C

18 нояб. 2017 г.

Basic introduction to the tools that will be adopted in the Data Science. I do hope there would be more information regarding Hadoop, Python, SQL, etc.

автор: alifiya l w

1 авг. 2019 г.

As it is a very technical to set everything and to start working on it but the mentor have tried to make it easy. For a beginor it can be challenging.

автор: Edward C

23 мая 2018 г.

Informative, but course did not provide exact information needed to complete the assignment without additional research (unless that was the purpose).

автор: Ajay K S

15 июля 2017 г.

Although I am vey much satisfied with this course but i felt a little low during the slides for explanation of types of data science .

Rest was superb.

автор: Jack L

16 янв. 2017 г.

Pretty light weight so far, but I certainly understand it's targeted at a broad range of people so the vanilla material is probably a chore for some.

автор: Zhuoxiang Y

29 нояб. 2016 г.

This is a clear introductory course, but the content is not as much as expected. Compared with other coursera course, this one is like one-week pack.

автор: Harshita D

8 мая 2020 г.

The course is well structured but I still prefer that students new to this must read from other sources as well to completely understand the topics.

автор: David A

6 июля 2017 г.

Good and easy introductory course. Finished it in about 3 hours total - don't expect this to be the pace for the rest of the specialization, though!

автор: Wissam A

4 янв. 2017 г.

Lots of information. I learned quite a lot. It was a nice and informative introduction to Data Science in all aspects technically and theoretically.

автор: Andrés F

2 апр. 2021 г.

Interesante introducción, quedas con el entorno listo para iniciar el aprendizaje. Se hecha un poco de menos un humano en las clases pero está bien

автор: Cesar M A T

1 авг. 2020 г.

This is a mere course on installing all the necessary tools and setting your environment, so the explanations are short lived and practice is null.

автор: Nicholas E B

6 мар. 2019 г.

Gives you the basics of how to use R and it's different file sharing platforms, is very useful when completing a project with various contributors

автор: Graham W

16 февр. 2016 г.

Good overview, I appreciated that the course gets you set up and makes you prove it. More command line and RStudio simple exercises would be nice.

автор: PATNAYAKUNI P

7 июля 2020 г.

coursers are well explained.some of the topics are not explained .i request you to provide links to study the topics which are briefly explained.

автор: Emilie B

15 нояб. 2016 г.

I've just started this course, that's why my rating is 4/5. But I'm very satisfied with the video lecture, and I find it very interesting so far.

автор: Melinda M

24 янв. 2016 г.

Useful couple of modules for helping you get set up with R and Git/GitHub. Really doesn't require a full month to do.

The quizzes were pointless.

автор: Tanisha Y

18 февр. 2021 г.

The explanation of the course is up to the mark. This course is overall good besides you need to follow other courses of the specialization too.

автор: Anand R

3 мар. 2020 г.

It was helpful for the beginners who is willing to learn data science and gained a knowledge about Github and other aspects. Thanks for Coursera

автор: Adam G

5 янв. 2018 г.

Good course that clearly explains basic R & data science concepts. Workload was very light - didn't get to practice or learn as much as I hoped.

автор: Bhojraj B

4 мая 2016 г.

This course is great to start with. It provides overview of what is in the course and how should we as students be prepared to learn the course.

автор: David R

7 мар. 2020 г.

Excellent course. I found it particularly hard in the first two weeks. Practical exercises were intense. Discussion forms really helped out.

автор: Karthik R

16 янв. 2017 г.

Very good in setting up for the next courses. I would like to see more on Git though because I could have definitely understood it a bit better

автор: Douglas R

16 июля 2016 г.

With my background I found this part of the course rather slow, but I can see why it's necessary for people with less of a software background.

автор: Jeff L

1 февр. 2021 г.

Computer voice was a little to quick to move on in some portions of the course. Particularly when trying to type code in R Studio or Git Hub.

автор: Nick M

29 июня 2020 г.

Nice way to teach but more example related to bio medical please add more example like finance and technology and please check out while study