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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: 29,887
Рецензии: 6,376

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

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.

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.

Фильтр по:

4201–4225 из 6,236 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Jose L P M

24 нояб. 2020 г.

I started with zero knowledge about data science and now I have learn about I practiced the basic tools that can be used for any data processing and presentation job.

Duration and deepnest of material is ok and I could learn at my pace while having additional documentation in case I need more deep knowledge of the tools and techniques.

I plan to continue with other Data Science courses.

автор: Victor K

7 нояб. 2018 г.

Good introduction to the Data Science Toolbox. I found the course very engaging, setting up foundation for further studies. Also, very nice platform supporting In my mind though taking this course makes sense only in conjunction with the other courses within this Data Science Specialisation as knowledge you get from Data Science Toolbox will not be sufficient for practical application.

автор: Oleksii P

22 нояб. 2016 г.

This is my first course on Coursera so i didn't really know what to expect and i apologise in advance for my ignorance. The course only takes you 8-10 hours or so to complete (even accounting for reading external sources) spread across a month. I enjoyed it, but it doesn't feel like i learned a lot and i think it could be a bit more intensive. Looking forward to R Programming (part 2)

автор: Martin J M

11 мар. 2017 г.

With my background, this was a lot of repetition of information that I already knew, but I was happy to have the reminder and it would be an excellent introduction to the specialization for others who do not have as much of a data science/computer science background. Overall, I feel that the course was worth my time and I feel it is a great start to the Data Science Specialization.

автор: Hong C

29 июня 2020 г.

It is really a long journey to me ( 6 months) and the study itself has ups and downs. The first few courses and last few course are relatively easy to me, but the statistics reference and regression model are really hard, and Capstone is the most difficult and time consuming course/project ever. I am glad it is all over and pretty sure it will be worth the time and effort on it.

автор: Jen V

8 окт. 2019 г.

I found it somewhat difficult to figure out how to push the file to GitHub (luckily I did find some helpful info in the forums), but otherwise found most of this course very easy. I can't figure out how to download the slides in such a way that allows me to click on hyperlinks, and I've seen other people say the same thing. I also would recommend making the code & graphs bigger.

автор: Keuntae K

10 сент. 2017 г.

Overall, a great course. However, I hope that the course will focus on explaining overview and principles of data science, not explaining how to use toolboxes such as R and Github. R and Github as data science toolboxes are not well explained in this course because it was quite difficult for me to follow what the instructor showed in his slides about using codes in R or Github.

автор: Маношин А А

20 мар. 2020 г.

This is a good course. You will recrive knowledge that was promised, but 1 mounth for this is too long. 2 weeks is long for this too, but at least appropriate. Also I have encountered some problems in the process that were not mentioned in course. It was not too hard to solve them but this is it.

Thank you for your course! I will definitely take the next course in the program.

автор: Rick M

12 сент. 2020 г.

Enjoyed the course and learned a lot overall. It was the kind of intro and overview of data science I was looking for. Several of the quizzes need a review of the answers in the multiple choice questions. As my first class taken in Coursera I found having to retake a quiz and game it to get the "correct" answer that was not actually the accurate answer a little frustrating.

автор: Jose A C

29 нояб. 2018 г.

I think the course is very good. I believe that it could be improved if the videos could reflect a more realistic approach on how to get to certain sites and how to download programs. Even though the students get the final result, in some cases it is frustrating and time consuming to determine if what we are doing is correct. In general, I am very satisfied with the course.

автор: Abir N

10 июля 2020 г.

This course is about nuts and bolts of R software, Rstudio interface, Git and Github with a brief inroduction to version control and other prerequisite of building a data science project. The only downside of the course is it uses automated videos which is a bit mechanical sounding.Though it is my personal opinion but that's the the cause of a missing star in my review.

автор: Dylan B

8 июля 2020 г.

All of the course content is excellent however there are issues with the peer review system. The final project is peer reviewed by only 2 people (per submission) so the mark has a very high amount of variance and so is essentially luck based, which means you have to submit many times until you get a pass mark on it. Other than this there are no problems with the course.

автор: neil v a

14 нояб. 2017 г.

It's all fine and well to just learn and do things by watching youTube and reading internet websites.

But your learning needs some structure, because at the beginning of the process, you don't know what is important and what is not ! I found the course good at helping give me an oversight to the subject, and getting me up and going with the software components needed.

автор: Max M

16 февр. 2019 г.

I believe an updated version would be beneficial, as some R packages have now somewhat different functionalities. Furthermore, I would have liked a bit more instructions into how to create a markdown file. Otherwise fairly easy course; not sure what to expect for the rest of the specialization then, although I've read very positive results. Therefore, I will continue.

автор: Leandro S

10 окт. 2017 г.

The material is good, the subject is interesting, the slides are ok, but the audio is suboptimal. In addition, there could be slightly more interaction (I mean the lecturer recording videos with him doing things at git bash, for example). This course is a simple and good preparation for the further courses on the Johns Hopkins Univerity Data Science track at Coursera.

автор: Sumit S

21 апр. 2020 г.

Really great content, but not a fan of automated videos (as of now). If booking a recording hall is a hassle, a background human voice with slides will be a much better option. If I were to complete a course just by reading study material, I could have chosen a Book instead of a MOOC.

Keeping this issue aside, Overall great content for getting started in Data Science.

автор: Dale O J

9 мар. 2018 г.

This is a good introduction to the tools necessary for Data Science. The lectures are comprehensive. Nevertheless, I view online tutorials for Git and GitHub as well as Dr. Leeks book The Elements of Data Analytic Style as being important supplements to the lectures to clarify and amplify the points that Dr. Leek is developing and attempting to impart to the student.

автор: Mateus S F

19 мая 2020 г.

Video-classes are presented by software voices (with an alternative of using only the text/slides provided by developers), which is a little bit annoying and distances the student from a motivating experience of having an actual professor, an example/model figure to be pursued. The content of the course is complete and well explained by the provided material though.

автор: KETIREDDY K R

21 апр. 2020 г.

Course content was too great but that robotic voice i know its still in development but that voice always irritated me and made me distracted .Sometimes i got errors even though i followed the course content exactly the same way you did but it is good to correct the errors on myself.Thank you for this awesome course but i hope you come up with a good robotic voice

автор: Alessandro V

18 апр. 2020 г.

I appreciated a lot the program regarding the toolbox, many good references and links are included.

I found a little vague the definition of P-value. I can understand that this was included inside an introductory section, however the criticality of this definition shouldn't be neglected. (I posted a specific comment in the forum of week 4)

Thank you

Alessandro Vasta

автор: Jonathan K

8 июня 2016 г.

A good but brief introduction to a number of useful skills. I learned a lot in a short amount of time but I still have a long way to go. I was somewhat disappointed in the lack of communication with a TA or instructor. The message boards were desolate and could not support any kind of a robust discussion of the conceptual issues involved with data science.

автор: Débora d C S

6 мар. 2016 г.

The instructors are great, but I think the content of this course should be embbeded in other module. Despiting being important, the topics covered here are very introductory. I recognizes, though, they are important to align expectation and to put everyone in a minimum ground of knowledge. However, I am not sure if Coursera should charge US$ 29,00 for it.

автор: Margaret

4 окт. 2016 г.

It's a good course, but it might be a little too introductory for someone who already has some familiarity with programming (although I hadn't used R, I had other experience with a similar language). So for me, I completed this course much more quickly than I anticipated and was really just very eager to move on to the other courses in the specialization.

автор: Sam E

27 мар. 2018 г.

This should give you a feel of data analytics and help you decide if you should proceed with the entire suite of classes. The course itself is not enough. It needs supporting materials and more practice sessions depending on your experience level with computer science. I enjoyed it and recommend it for people with no background in computer science.

автор: SRINJOY R

7 мая 2020 г.

The lectures are meticulously built for learning perspective. But it would be have been more great if it can be delivered by some faculty in this field. Then i could have understood better. Moreover some real time problem should be given to us so that we can analyze the data and get an experience of how this this course is helping us practically.