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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: 32,478
Рецензии: 6,929

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:

276–300 из 6,818 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Nirav D

2 апр. 2016 г.

This is the first course in the Data Science Specialization series by Johns Hopkins University taught on Coursera. It introduces all the tools necessary for subsequent courses on data science and gives a driving motivation for the specialization.

автор: Pablo G d S

17 мая 2021 г.

O curso é muito bom. Para alguns pode ser que seja de dificil aprendizado, devido a o fato de ser um robô. Para mim não fez a menor diferença, uma vez que o conteudo era de excelente qualidade. Obrigado pela oportunidade John Hopkins University.

автор: Carolina S

8 окт. 2020 г.

This course is the right choice for those who want to get solid foundations of R and version control. I definitely recommend it!

The materials provided are of very good quality and they come in different formats: audio and written, with pictures.

автор: SURJEET K S

17 окт. 2018 г.

This is the first course in my last few online courses of R where I learnt how to initiate Git and Github and do things in a very formal sequential manner. Very good for people who are not too technical in nature. Simple and easy to understand.

автор: Ana D

30 июня 2017 г.

Nice course, very complete, although being new to it I would have loved a longer lecture on thisas well as a practic examn or something similar with the objective of developing this skills sufficiently for future courses fron the specialization.

автор: Syed M A T

28 июня 2018 г.

I have tried to learn a couple of times but due to busy work schedule i always left in the middle of a course or tutorial. However this course due to its interactive nature kept my attention. Finally i completed my first course in Data Science.

автор: Pradeep p

10 нояб. 2016 г.

Course Structure is good. I could able to gain the basic knowledge & ideas about tools & questions Data Analysts work with, then could also get practical understanding about the sharing & version control tools.Looking Forward for other Courses.

автор: Jeff D

3 окт. 2020 г.

Thank you very much for this course, this course helps me to have a good understanding on how a data scientist works and what are the different tools needed to work and collaborate with a different professional to work with a specific dataset.

автор: Abbid A

28 июня 2020 г.

Guides students through the basic tools all data scientists need and sets them up to both learn and apply them. A very good introduction that not only covers the theory behind data science but also shows how it can be used in the modern world.

автор: Dostonbek K

3 нояб. 2020 г.

So far, although I am studying computer science, I was too far from the nowadays-popular concepts Big Data, data-analysis (what I am very interested in) and This course covered and gave me the motivation to keep on studying the specialization

автор: RASMI

31 мая 2020 г.

I really loved the quality of the content being provided. I appreciate the fact that the courses have been designed for being beginner friendly, but at the same time it ensures that the student gets to know about all the relevant content too.

автор: Maharshi D

16 июля 2021 г.

Great starter for aspiring data scientists! I would recommend this course for conceptual understanding of the field of data science and big data analytics, as it is not good to directly delve into it without such prior knowledge. Great work!

автор: Dr. S M A T

12 июня 2020 г.

Excellent Course designed to learn insights relating data science using R software. basic Coding and analytics basics are taught in this course. Moreover learning relating how a data scientist can collaborate on GitHub is an added advantage.

автор: James M

17 февр. 2021 г.

Very basic, but I guess that is what is what is meant to be. Not sure I really liked the computerized voice over slide aspect. I get the point of how that is more efficient, but it certainly takes something away from the lecture experience.

автор: John

22 апр. 2018 г.

This course was a lot of initial set up for the rest of the program. Basically just setting up programs and getting accounts ready for future study. All in all a good start, I expect material and course work will significantly pick up now

автор: Ajay S C

22 авг. 2017 г.

its a nice course to introduce you with the tools required ahead in this journey of data science learning. It helps you in taking that first important step for moving confidently ahead in this great learning. Keep up the good work coursera

автор: Eugene V

30 авг. 2020 г.

Content is not so engaging but I think the new format (text-speech) will help improve the course delivery. Topics are discussed thoroughly and if a certain topic will not be discussed in detail, links to additional materials are provided.

автор: Michael M

28 мая 2020 г.

Exactly what I wanted from an introduction. I feel prepared to begin the more challenging courses in this specialization.

This course isn't so much a value-add on its own, but is designed to set students up for success in a specialization.

автор: Tommy Y

12 апр. 2021 г.

It was an informative foundation to start learning R-programming to Data Analysis. Although it was not easy at all for me to complete this course, I really loved to learn this toolbox including important concepts of statistical analysis.

автор: Muhamed N A A

17 дек. 2017 г.

Though it was a ground basic skills, but yet solid to build upon. This course helped me a lot getting a clear picture of what is data & data science and the necessary tools required to analyize & generate actionable data.

Thanks Coursera

автор: Hemanshu S

5 дек. 2017 г.

This is really good course and overview about Data science. I had great start for course and Git and basic commands of Linux after several years.

This course will definitely give me exposure to think in best future stream of IT industry

автор: Owie

23 авг. 2017 г.

A great introduction to Data Science and the necessary tools required for the remaining course. Fairly simple, but more catered to a warm up for the remaining courses. Would encourage everyone to continue to complete the specialization.

автор: Kylie A

15 июня 2021 г.

This class was pretty easy and was a good introduction on how to set up R and RStudio. I wish it had taken a little more time with introducing version control (Git) and teaching you how to use it, but overall it was a good intro class!

автор: Hasan B

11 янв. 2020 г.

A very good, organized, and interesting course that teaches you the fundamental introductory elements conserning data science, and helps you learn how to set up essential tools in order to begin learning R programing and using RStudio.

автор: Sean S

26 дек. 2019 г.

I thought this course gave a great introduction to the topics being covered in the Data Science Specialization. I think the only thing I would of liked to of seen is more case studies and maybe some more supplemental reading materials!