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

4.6
звезд
Оценки: 32,485
Рецензии: 6,930

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:

4751–4775 из 6,821 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Annie C

24 мая 2021 г.

I enjoyed the simple informative lectures. It was easy to get along and practice on our machines. I would have liked some course materials on the lectures along with the classes to follow through as refresher guides for the future.

автор: Nil G

14 февр. 2016 г.

Very good composed, explains in a very good manner the complex topic, a general overview about the tools and their connection to each other would be great and helping, as there are many tools to install and understand the functions.

автор: Ayman I

2 мар. 2021 г.

Clear and relevant content that is easy to understand and follow. My only gripe is that the lectures given in text-to-speech robot voice are dreadful. It's not a deal-breaker since there was the option to read everything instead.

автор: Joshua M

18 мар. 2018 г.

A very good course to learn the different applications needed to start data science. Lectures and examples are easy to understand. Highly recommended to those who would like to know and start a career in the data science field.

автор: Gágik A

31 июля 2016 г.

The course itself only introduces the main aspects and helps with installation of the tools, while no actual programming is taught. But it is useful for having better understanding of the following courses in the specialization.

автор: Aoife M

7 нояб. 2019 г.

Informative course which provides new information in chunks to make it accessible for all. Varied resources to aid all types of learners and regular assessments are helpful in understanding the learning objectives of each week.

автор: Hannah S

16 февр. 2017 г.

Super friendly to new beginners with clear definitions and easy-following learning path. Although a bit of slow for me. I'd recommend anyone without programming background to launch their study in data science with this course.

автор: Gustavo M S

4 янв. 2021 г.

I've enjoyed the course and liked the course format, but bear in mind this is a very basic course. in it, you will learn how to install R and a version control software, and you will learn the most basic data science concepts.

автор: Andrey S

6 июля 2018 г.

A nicely designed introductory course of the specialization. Doesn't' have any sufficient value as a standalone course, still, has crucial importance for the thorough and successful study of other courses in the specialization

автор: Antonio G M

3 янв. 2019 г.

It is a nice course. From my point of view it would be great if it included more advanced content but I understand that it is an introductory course so it is ok. In that sense it is great for people that is new to this topic.

автор: Kari R

4 дек. 2020 г.

Great concepts for someone new to R, R Markdown and data science. Im only putting 4 stars because I wasn't a fan of learning from a bot, and video content didn't closely demonstrate the how to during technical walkthroughs.

автор: Uğurcan K

30 авг. 2020 г.

It's indisputable that topics in this course are so important to build a foundation for being a data scientist. The only problem I've faced is that the lessons are much slight. It should have given more details about topics.

автор: Atef A

17 нояб. 2020 г.

The course is well explained and give a great introduction to Data Science, but i would found it easier if the speed of instructions was slower and the tasks had more details in them to follow. In general, very good course.

автор: Jonelle C

11 сент. 2020 г.

Very easy to understand program, step by step helps a lot and the videos are actually very informative and helpful. I had a few problems with downloading and integrating Github into my Rstudio but it was easy to figure out.

автор: Zhao Y

21 окт. 2018 г.

got a general view of tools that data scientists need to know about or master. really useful and inspiring. this course encourages me to figure out problems I might meet during data analyzing like a data scientist. cheers.

автор: Lindtner A C

8 июня 2017 г.

it was hard to find to rewatch to git tutorials, as it was very fractured. Also, hated watching MAC tutorials to get all the vids done. Otherwise useful and well prepared material, looking forward for the rest of the spec.

автор: Sapna G

20 янв. 2017 г.

My first online course and it could not have been any better. Gives me the confidence that I can learn online and add to my knowledge new things. Course material was explained in simple terms which is not the case always.

автор: Carlos C

19 авг. 2020 г.

Good course to get you started on the Data Science Specialization from Johns Hopkins University. Maybe it could have had more weeks or more content, or at least more complicated exercises, but overall it's ok to start.

автор: Leah F

26 июня 2020 г.

I think the submission for the markdown file should be allowed to be EITHER an .md file OR an .Rmd file. Seems kind of silly to lose credit for pushing a .Rmd file directly from RStudio rather than saving a text file.

автор: Jeffrey G

30 мая 2017 г.

It was a fine overview. I think the suggested text was also okay, but for a $10 suggested price, I would say that there should either be a reading assignment or it should be made more clear that the text is optional.

автор: Álvaro S F

27 авг. 2020 г.

I wish I could listen to the professors' actual voices, because doing so in a more natural way would help me avoid tuning out from the lessons from time to time. But I fully understand why they are doing it this way.

автор: Anne N N T A

7 июля 2020 г.

OK introductory course to the tools used by data scientists, however it's a bit odd this is a standalone course; IMO the course content isn't enough and should be combined with the next course in this specialization.

автор: Karen P

31 мар. 2018 г.

The class definitely eased a new learner who is unfamiliar with the concepts and skills. There are areas in this class that seemed outdated an made the class a bit confusing. Hopefully, those can be update in time.

автор: Karen H

10 мар. 2016 г.

This introductory course is a good overview, goes pretty quickly through the basics, and gets you prepped for delving deeper in the subject matter. Planning to sign up for the next course in this certificate program.

автор: Nick S

1 нояб. 2019 г.

This course was fantastic, I just wish that there was more interactive work within the lesson's versus only in the final project. I believe that would help the material set in faster and better. Overall good course.