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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: 32,171
Рецензии: 6,875

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:

4876–4900 из 6,757 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Christopher S

7 окт. 2017 г.

Not the most engaging material, but the course does a good job of covering the fundamentals of R and GitHub and helps you download the necessary software.

автор: Adrian P J R

7 июня 2017 г.

Some typos in the slides need correction. Narration can be made more lively. Slides still quite classical in format and may be improved for better impact.

автор: Liz T

16 янв. 2016 г.

Good overview of Data Science, but geared more towards people who have little background in computer science. Info that *was* presented was very thorough.

автор: arpit p

24 сент. 2020 г.

Auto-generated voice is a major down-point. Then this is supposed to be a beigner friendly course, but it isnt really looks like that in certain aspects.

автор: Muqaddas R

18 нояб. 2018 г.

It's a really good course for the absolute beginners, but for me it was quite slow. I just took this course because it is the part of the specialization.

автор: Ramy H

30 июля 2017 г.

There are a lot of info on the video. Would be good to share a copy of the slides for the Git links/instructions so we can use them as a reference later.

автор: 허욱

6 дек. 2020 г.

This lecture tells you a lot about new places for experiment and study such as github, r studio. Hope many people check out this lecture and gain a lot!

автор: Praveen S

15 дек. 2016 г.

This course provide a good introduction to github , Rstudio and command line interface.

it also gives a information about different ways to analyse data.

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: Tiago O B

17 авг. 2021 г.

Hands-on, comprehensive course about the basic tools you'll use as a R-based Data Scientist. Robotic narration of the videos is a downer though.