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: 32,297
•
Рецензии: 6,901

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:

6401–6425 из 6,786 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Avi C

•
20 июля 2016 г.

Just an intro and learning a bit about Git and Git hub.

автор: Victoria M

•
14 мар. 2016 г.

Doesn't provide a strong enough base for R Programming.

автор: Anagha J

•
7 февр. 2016 г.

Good and basic understanding of terms.

Can be made vast.

автор: Jay D

•
8 апр. 2020 г.

Just the basic setup... pretty slow but informational.

автор: Morgan A G

•
18 февр. 2018 г.

Very easy, I am unsure if this is a good thing or not.

автор: Rafael C

•
16 февр. 2017 г.

Good course, but worth only for really basic learners.

автор: Vivek K

•
8 янв. 2017 г.

Not really a course, should just be Week 1 in course 2

автор: Veli-Pekka V

•
22 окт. 2016 г.

Covered basic elements of data science and statistics.

автор: Mauro P

•
7 февр. 2018 г.

I would like to see more practical exercises about R

автор: jiminkang

•
27 июля 2021 г.

An AI voice makes me hard to concentrate on lecture

автор: zin

•
26 сент. 2019 г.

It was helpful in getting to know R and R packages.

автор: Smit A K

•
26 мар. 2019 г.

The entire course can be explained in 2-3 lectures.

автор: Alaa E D H

•
14 янв. 2018 г.

this part is not necessary, keep it optional please

автор: Juliana C

•
25 сент. 2017 г.

Very introductory course, give an overview of tools

автор: Renata G

•
3 июля 2018 г.

Good course, but lectures are sort of superficial.

автор: Rob L

•
22 мар. 2018 г.

Maybe a good basic intro course, but quite simple.

автор: Suhas K

•
24 апр. 2019 г.

I wish there were more exercises for the project.

автор: Vasilis Z

•
16 янв. 2017 г.

nothing special, no significant knowledge gained.

автор: James P

•
28 мая 2016 г.

Later courses are better. This one is quite easy.

автор: patrice j

•
23 февр. 2016 г.

Good but needs more assistance with the students.

автор: Dwi N R

•
30 авг. 2020 г.

This Course is great. but the speaker is too bad

автор: Xiaojun L

•
30 мар. 2017 г.

The git and GitHub section is not very clear....

автор: Rishi R

•
4 мар. 2017 г.

Too basic, not very informative. Waste of money.

автор: Vitor F M F

•
9 янв. 2017 г.

The course is too short and I didn't learn much.

автор: Rafael L

•
4 февр. 2016 г.

Quite basic, but good to set the needed programs

Coursera Footer

Начните карьеру или продвиньтесь по карьерной лестнице

Смотреть популярные темы

Развивайте свою команду

Получите диплом или сертификат онлайн

Coursera

Сообщество

Еще

Учитесь, где угодно
Загрузить из App StoreЗагрузить в Google Play
Placeholder