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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: 30,963
Рецензии: 6,597

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:

4451–4475 из 6,457 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Scott D

7 мая 2020 г.

A good course with clear instruction that gives you a basic review of using data and installing R and related programs. Occasionally necessary steps in R are omitted and one has to do some googling. Not a fatal flaw, but frustrating for a beginner.

автор: Roberto R

23 июля 2020 г.

It felt a bit like a RPG tutorial where your big accomplishment is learning how to run or crouch, but I guess it makes sense for it to be part of the Specialization track. I would recommend it as part of a series, more than as a standalone course.

автор: Carolyn A

8 февр. 2016 г.

Great introduction to the different tools that a data scientist will encounter and use, including RStudio, Git, and GitHub. I would have appreciated more practical experience linking Git and GitHub, as that is critical for version control of code.

автор: RICARDO F F D L J

6 авг. 2020 г.

I liked the course. I think that at times it is not clear and at others it is wordy. I gave 4 stars mainly because the course menu promises subtitles in Portuguese and in more than 60% of the videos there are only subtitles in Korean and English.

автор: Samuel M A

5 апр. 2020 г.

I had some issues in following all the steps that are shown in the lessons. I think the demos skip important steps. But, on the other hand, it forces to search and look for solutions to these issues on the web. Overall: good introductory course!

автор: Jeroen v B

12 сент. 2016 г.

It's a good course, you're not going in-depth but this is just an introductory course for the Data Science master and the tools you will use. You will learn the basics of Git and get acquainted with R and is thus somewhat essential for starters.

автор: Wendell B

19 мар. 2020 г.

Reviews or Test should rely more heavily on the instruction that goes into detail on a topic matter and questions that were asked on quizes. For example, the datasharing question was worth 2 points, when that topic was only cover very briefly.

автор: Reinier B

5 февр. 2018 г.

Although I found the course material in general clear and well-explained, I found the lecture on 'Basic Git Commands' poorly explained and sometimes poorly audible as well. For a non-native speaker of the English language it was hard to follow.

автор: Shashank S

29 окт. 2016 г.

This is a good course for someone who is not familiar with the basics of Git,Github and needs to install R,Rstudio and related packages. If you are not the kind of person described above you will be able to breeze through the course very fast.

автор: Azin S

21 нояб. 2017 г.

The course is very fluent and attractive. You may run into some questions while following the course which you can easily find the answer to by googling it. As a beginner in both Data Science and programming, I'm very happy with this course.

автор: Sarwar A

20 янв. 2020 г.

The lectures were good.After all it's robot orienting converstaion it has lot of pace in speech I think that is not good for me.Because It was little bit hard to grasp the message.The pace is only the concerned.Overall lectures were good.

автор: Kevin J Y

10 сент. 2017 г.

There are some typographical errors in the quizzes and the english subtitles. Not really a big deal. The Week 2 about GitBash made me a little confused because the video about loading git bash happened before the video about installing it.

автор: Daniel A

11 сент. 2020 г.

I am giving the course 4 stars because it is online. should it be practical, then it would have earned 5 stars. Some of the concepts were unclear especially "Experimental Designs". Hope there will be more practical examples to work with.

автор: tierny a c

22 июля 2018 г.

I don't feel as though the 16 minute video on command lines was efficient. I spent a gross amount of time (over 3 hours) on youtube for supplemental instruction just to complete the final project. Otherwise, this course was sufficient.

автор: Victor A T

26 янв. 2020 г.

A very good course for beginner to start off with. This course really helps setup the fundamental toolkit to create a efficient workflow. The git/github version control linking with R/Rstudio is the best thing I got from this caourse.

автор: morgana

24 мая 2017 г.

Excelent course. The schedule was basic however have approached a thematic complex and important.

The time to complete the tasks week was great.

But I felt need to learn more about git and github. I don't know if it was on follow weeks.

автор: Marc E S

24 февр. 2016 г.

Easy to follow. Might be too easy for some people with experience in data analysis. However, the instructors also talk about some frameworks and insights from their experience which could be helpful for even those who have experience.

автор: Guillermo D

17 окт. 2016 г.

El método que sigue el curso me ha sorprendido para bien. Hay determinadas herramientas que aún no comprendo bien qué utilidad podría tener para mí. Quizá porque este primer mes sea muy general. Veremos qué aprendemos este nuevo mes.

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 Y M 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.