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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: 32,310
Рецензии: 6,902

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:

6351–6375 из 6,787 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Divyansh M T

28 июля 2017 г.

Very insubstantial, in terms of material. Much more can be done in 4 weeks.

автор: Mamar H

25 июня 2016 г.

A little bite long regarding the concepts number developped in this course.

автор: Ian W

30 авг. 2017 г.

Easy course. Everything covered in this course can be found easily online.

автор: Mohamed A E M

20 июня 2017 г.

Nice introduction but not many new ideas i had recive, but thank you team.

автор: Hamza T

9 авг. 2016 г.

A great course. But I still think there is a lot of room for improvement.

автор: MEHEDI H

1 июля 2020 г.

Need more friendly lecture, that's why we can understand topics easily..

автор: Vishesh k

16 июня 2018 г.

Sound quality for every lecture is very disappointing ,very poor quality

автор: Abhey K

27 мая 2017 г.

A good introduction to data science and the associated issues around it.

автор: Perruchoud A

13 авг. 2016 г.

Extremely useful info, but the material is too shallow to be satisfying.

автор: Daniel R

7 февр. 2016 г.

It is a good course, however, I would have liked deeper knowledge in git

автор: caio l

12 сент. 2017 г.

Really basic, it's only necessary if you have no knowledge in the area.

автор: Mitchell S G

12 нояб. 2016 г.

Audio on videos was sometimes unclear. Course content was interesting.

автор: Nate S

26 июня 2017 г.

Mostly auditory discussion of concepts. Not so much visual or hands-on

автор: Aditya C

4 янв. 2019 г.

Very basic...One must do the entire specialization to make any sense.

автор: Rupert

31 авг. 2017 г.

Very basic. Probably unnecessary for anyone with some experience in R

автор: isha v

12 авг. 2017 г.

Please include some optional Quizzes/Assignments also for practising.

автор: Prasen M

17 апр. 2017 г.

Very basic introductory course. Looking forward to the other modules.

автор: Juliana N

12 мая 2016 г.

The instructions for using git and git hub left a lot to be desired.

автор: Revanth T

15 февр. 2016 г.

Very Basic Course. Doesn't require the time that was allotted to it.

автор: Matthew K

10 мар. 2016 г.

Probably more useful for someone with no background in programming.

автор: Arslan R

8 февр. 2019 г.

good for introduction. The sound quality is low. Can be shortened.

автор: Rustick T

22 нояб. 2017 г.

Ne présente qu'un ensemble restreint des outils du data scientist.

автор: rotem n

13 сент. 2016 г.

too easy,

i wish we had more things do submit so we will learn more

автор: Haojun S

12 сент. 2016 г.

The introduction of how to use github is undetailed in my opinion.

автор: Rakesh

10 апр. 2016 г.

Too simple. Personally feel this can be clubbed with R Programming