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: 31,502
Рецензии: 6,696

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:

4601–4625 из 6,552 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Emanuele M

10 февр. 2016 г.

it's well done, i was expecting more details in the lessons while students it's requested to search many things on internet in order to learn, for instance GitBash. However it's a good first course

автор: Richard B

8 окт. 2018 г.

Having to skip through all the Mac videos is annoying. Just make an option at the beginning if you're working on mac or pc or both so i don't have to deal with skipping some videos and not others.

автор: Joshua

26 февр. 2018 г.

This course seemed a little to basic. I know it gets much harder going forward (as I've already started on the next course), but I feel like more knowledge could have been packed into this course.

автор: Stephanie C

21 сент. 2017 г.

Some of the course material seems a little out of order, and some things I went externally to figure out, but overall I think that it is a great class for someone looking to get into data science.

автор: shubham t

2 июля 2020 г.

Great course to know these tools. Need more explanation with the videos and more interactive assignments to understand better. Also, language change is one thing that needs to acquire eventually.

автор: Matt R

10 февр. 2018 г.

Additional exercises on setup and use of git would have been helpful, particularly with the local and remote synchronization. You may want to phone-a-friend on git if you need to use it daily...

автор: yanto

25 сент. 2016 г.

Well basically tutors only providing slides, speech, forums and ebook in this course...rest is self-learning, self-understanding, self-asking... if not, then you'll not pass this course i think..

автор: Koduru K C

2 мая 2016 г.

The course covered the foundations well especially how and what softwares to install etc. I would have given five stars if the course contains covered some extra details about R and datascience.

автор: Kjell E N

6 мая 2020 г.

Fine, but (as the authors admit up front) there is room for improvement in the canned text-to-speech. Does feel kind of impersonal. But I am going to continue with this course of study anyway.

автор: Wei D

14 июля 2019 г.

Good starting point for beginners to learn about R. Basic experience with git is a must although it is possible for complete beginners. Will just take more time to do the homework and quizzes.

автор: Mohamed I A

23 дек. 2017 г.

Nice course as a start... I just felt I need more knowledge in the area of github (the idea of pushing, pulling and forking). But maybe it will be more clear by time as this was just an intro.

автор: Andika Z F

26 июля 2020 г.

I think it will be better if we have not a computer programmed lecturer to give the material because I find it hard to understand what she says and I will prefer more on actual human lecturer

автор: Alina S T

12 сент. 2019 г.

I took the same course 6 years ago and it was a little more challenging. It actually had coding assignments. and not only in R, but in Python and SQL too. That was a more complex "toolbox"...

автор: Anoop B

3 мая 2020 г.

The course was excellent however the robotic sound of the AI was a challenge. Once I overcame my inhibition to learn from an AI system, I started to like the course and learnt a lot from it.

автор: Yitong L

9 июня 2019 г.

This course missed some important step, for example, the git push which is not be cleared how to push and does not teach us the git pull as the REANDME.MD is not in the local repo initially.

автор: Ashish K

8 дек. 2018 г.

Should spend a little more time explaining how interaction between Git and Github works. It is not very intuitive and requires revisiting the video as well as google search to understand it.

автор: Arjun S

27 авг. 2017 г.

A very easy course, can be completed in 3-4 days at max. Having done R programming course first, this was a breeze taking a couple days. Good for people starting off with the specialization.

автор: Sander H

25 апр. 2017 г.

What I like (so far) is that you have to search for information via internet and by practising your knowledge in GIT and GIThub you can discover what you have learned or what should improve.

автор: Manu B

10 окт. 2020 г.

The course was informative enough for the tools needed. But I think a separate course just for sake of knowing tools makes not much sense. I wish there was more in this course to implement.

автор: Demi

26 апр. 2017 г.

Some detail of the Quiz and course project were not mentioned in those video courses, it made me have to google or find out the answer in other ways. But through this, I did learned more .

автор: Samiksha S

29 июня 2020 г.

Good course for beginners to data science world.

However some more application questions must be added in each section to acquaint the learners with a thorough knowledge and understanding.

автор: Juan D M

20 окт. 2017 г.

Describe aspectos básicos para la utilización de herramientas, tipo de preguntas y datos que debe manejar un científico de datos; es un buen abre bocas para el resto de la especialización.

автор: Camila T J

14 июля 2020 г.

In generall it was a good course, but sometimes the instructions weren't sufficiently clear to me, and I had to figure things on my own which didn't always resulted in a positive outcome.

автор: Léa F

23 июля 2017 г.

Good introduction, although the teacher spoke in a very low voice (it was sometimes hard/impossible to catch what he said) and some of the videos could have been a little more exhaustive.

автор: Zach D

29 сент. 2016 г.

A nice slow intro to Coursera, but not a ton of actual content. Good to get your feet wet, but skippable if you are familiar with the basics of the toolbox or taking courses on Coursera.