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: 32,990
Рецензии: 7,046

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:

4976–5000 из 6,944 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Ruben D W

9 мар. 2016 г.

Good introductory course, but could offer a bit more of a challenge, with actual (basic) problems. Learned about R, although not programming, and more about GitBash and GitHub.

автор: LI D

29 дек. 2017 г.

It's fairly good! Concise intro to DS. However, you will need to spend more time in CLI and GitHub by looking for information in the forum or googling step by step procedures.

автор: Rachel K

27 дек. 2017 г.

I was able to set all the programs up that I will need to program in R, but I still feel like I don't really know the basic overview of what I will be doing in this specialty.

автор: Philip W

18 окт. 2016 г.

Useful introduction to the world. Could have been a bit longer and gone into more detail about a variety of other factors is my only comment, but overall pretty happy with it.

автор: ramanathan l

7 апр. 2016 г.

This is a fantastic course, being a person who already works with loads of data I can surely stand by the statement that "This is a fantastic base line to all your data needs"

автор: Vishal M

22 мая 2018 г.

Could provide live demo of creating markdown file & pushing into the remote repository.

Overall the learning was awesome. Was a good guide to go forward to subsequent courses.

автор: Greg S

15 нояб. 2017 г.

Straightforward (although I am already familiar with markdown and git, so that helps). Lots of good groundwork here though, setting the scene for the next round of material

автор: Shaik M U

20 февр. 2017 г.

Coursera has changed the way of learning,Yes anyone can learn anything and everything at the comfort of their own time and place......

Thanks Team COURSERA....Keep Going....

автор: Brandon B

22 мая 2016 г.

It's a good introduction to data science. However, without prior knowledge of statistics, programming, and research methods the course would be fairly difficult to follow.

автор: sharraf t

2 авг. 2020 г.

the robotic voice got a bit annoying towards the end of the course, but overall i like the course and found it informative, cant wait to continue with the specialization .

автор: Willie C

21 янв. 2020 г.

This course is fairly rudimentary, but it does a really nice job of getting you all set up with the tools you'll need to tackle the rest of the Data Science specialization

автор: Hannah B

4 июня 2019 г.

The part where GitHub, Git and R are connected was very interesting. However, as a fairly experienced R user and scientist, I wish I could have skipped some of the parts.

автор: Ashley B

31 мар. 2021 г.

The robotic videos are crazy different from seeing an actual person on a video like in the IT Specialization. That's my only real complaint, otherwise, I learned a lot.

автор: Luis E R R

29 апр. 2020 г.

I found this course to be quite useful, albeit introductory. I kind of was hoping for more material. Nevertheless, for a beginner, I believe it is completely appropiate.

автор: Neha S

10 окт. 2016 г.

Good start for the beginners especially for people new to the data science. Topic are not covered in depth but as its just a introductory course so its totally fine..

автор: Mechell S

12 дек. 2020 г.

A great introduction to the world of data science. This course gives a good foundation of what is expected as a data scientist and the tools required to complete task!

автор: Alex B

26 авг. 2019 г.

Well explained, step by step instructions on how to install and use the tools

Potential to improve: use more visuals in de instructions (many times now it is only text)

автор: Mark M

1 июля 2018 г.

Provides a good overview of what will be learned in the Data Science specialization. Good introduction to the software tools required and how to set them up correctly.

автор: Luis A V C

10 мар. 2016 г.

Basic tools for shareing and develop that right now I don´t consider usefull. In the other hand, the content is well explained so right now I new how to use new tools.

автор: Bhargava P

23 июня 2017 г.

I think it did give a bit of an overview but I expected a little more stuff on actual Data Science and not on how the course actually works...

But good start.

Thank you

автор: Donovan W

17 мар. 2018 г.

Good for getting started, installing the necessary software. Wish github commands and markdown were explained a bit more, but that is likely done in a later course.

автор: Kristin A

8 июня 2017 г.

A nice survey that you can complete over a weekend if you do it in a concentrated dose. It really is just an introduction, though, and it only scratches the surface.

автор: Calvin B

15 окт. 2016 г.

Started out a bit slow with material that I felt was somewhat extraneous, but then got to the good part (hard part?) of installing R and getting it working with GIT.

автор: Lisa P

6 февр. 2016 г.

This course is good if you have zero background in R and using GitHub. It'll walk you through installing what's necessary and give you basic tips on how to use them.