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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

Оценки: 19,109
Рецензии: 3,834

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
Introductory course
(рецензий: 1056)
Foundational tools
(рецензий: 243)

Лучшие рецензии


Sep 08, 2017

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.


Jul 22, 2017

Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.

Фильтр по:

1–25 из 3,753 отзывов о курсе Набор инструментальных средств для специалистов по обработке данных

автор: Jitin V

Aug 13, 2018

Good to set you up for advance courses.

автор: Anthony V

Aug 16, 2018

Great course, really helps get you into the right mindset for becoming a data scientist.

автор: Frederik C

Aug 13, 2018

Great intro

автор: kaan b

May 28, 2019

Not met what offered. I really don't know why but Instructor was in a hurry and like, he was in the position of instructor by obligation.

Maybe, He has knowledge of the subject, but definitely does not have even basic skills of teaching.

Because of this course, I am not planning to follow other courses on this specialization.

автор: Khaleel u r

May 22, 2019

execellent i am very to gland get this certificate .. it is so valueable for me. the first one of data science track

автор: Andrea R C

Apr 11, 2019

A great intro to the course. I am not the biggest fan of the automated voice, but it gets the job done. I do like the secondary lessons written out with bulleted lists and close-ups of the slides. That is like a helpful review.

автор: Paul R

Mar 13, 2019

Basic introduction for the specialization, principles of data science, and installing stuff, it's fine to get started but could get hands dirty with R more quickly. Overall the plethora of 4-5 star reviews for this specialization seem generous. You will learn a good deal but there is heavy focus on R and academics of data science (Rmarkdown, Knitr, shiny apps etc), only 3 courses (6,7,8) get into meat of statistics/regression models and ML; the capstone project is interesting but doesn't use much of this stuff, it gets bogged down in technical work with new R libraries for text processing. The material is a few years old and not being maintained, discussion forums and interest/participation feels stale. Take some time to look at syllabus and compare to other courses for what you want to learn before committing many months to this specialization.

автор: sonal g

Feb 03, 2019

Providing feedback means giving students an explanation of what they are doing correctly AND incorrectly. However, the focus of the feedback should be based essentially on what the students is doing right. It is most productive to a student’s learning when they are provided with an explanation and example as to what is accurate and inaccurate about their work.

Use the concept of a “feedback sandwich” to guide your feedback: Compliment, Correct, Compliment.


Jan 24, 2019

Coursera has given new life to me

автор: Aman U

Jan 05, 2019

Good but need more explanations for topics.

автор: David S

Dec 20, 2018

This course was in many ways the first day of lectures, get your syllabus, buy your books, install your tools, etc. I would give it 5 stars but the lectures inclusion of internet addresses that aren't links and aren't included in the transcript led to a lot of time paused and typing out long addresses.

автор: Usenaliev N

Dec 08, 2018

Would be great to have more reading materials

автор: Tolga T

Nov 24, 2018


%100 pure advertising. There is a moment I felt like I learned some thing, but rest of the course I played with x2.0, of there was more I would have get it.

Putting this into Specialization requirements is smart from your perspective, you are basically saying if you want to reach Capstone pay me $50 more, but at least fix the typos you made during video, just a little respect to your subscribers. But right now, I highly doubt that Capstone Project will be something serious that I want to mention in my Linkedn. There is also downside of what you do. But since you are in between the top rated courses either nobody uses Coursera anymore or people are silent enough and patient enough.

You are all Scientists like me, I'm also biostatistician but I would never ever post a course like this to any platform. I'd rather use Google or Facebook ads to lead people here.

If somebody wise enough to get Data Science Course, he should be skillful enough to download R, click next and install it, and R has help for it, shows you step by step. GitHub is free platform, anyone who can signup for Coursera can signup for GitHub, too.

I know there is no requirements for this course or specialization course, it is 0 to Scientist but seriously you are talking about R codes, arrays, loops, regression, model fit but signing up for GitHub.

Your target group in Coursera is either Data Scientist or becoming one, so they know what the Data Scientist job posts requires.

It requires coding blind folded R/Python/Java/one of C family at least 2 of them, hopefully all of them.

It requires SQL, MySQL, NoSQL, any kind of SQL or database solution mankind ever used.

It requires Math, Statistics, Analytics, Algebra, Finance, Economics + all kinds of computational sciences

It requires management, social relations, advertising, psychology, anthropology + rest of the social sciences.


so we are trying to be that guy, no need to show installing R or GitHub, I'm sure you will do it again doing rest of the Specialization.


Aug 31, 2018


автор: Jasmine P G

Aug 16, 2018

The course is clear and good to learn,

автор: Pratyush M

Aug 13, 2018

A bit basic, but a great start for beginners.


Aug 10, 2018

Teaching of lessons are simply amazing.

автор: William C

Sep 26, 2017

I really don't know much about this stuff, I think the jury's still out on whether the last four weeks will be helpful in the future. We'll see how much I think I've learned at the end of the course

автор: Raul G R

Jun 19, 2019

Too basic

автор: Nnene E

Jun 18, 2019

I enjoyed this course


Jun 18, 2019

Amazing course. Most importantly it kills your fear for Data Science

автор: Diego A Q

Jun 18, 2019

A great introduction to data science and also to the R Studio environment. R Studio is going to be very used through this specialization, so it is very useful to be familiar with it.

автор: Avnish S

Jun 17, 2019

Nice and helpful to start with R.

автор: Nikolay B

Jun 17, 2019

Overall an interesting program is offered. Just started, an update is expected towards the end of the course. So far found an issue w/ quiz #1 (incorrect grading due a broken internal logic (?) where 2 different 'correct' answers are offered during subsequent quiz sessions). Also, I would say that the intro videos are too short to be useful. Anticipated scope is well aligned w/ modern trends that are re-branded from the underlying concepts known for a long time; such concepts were always being in the arsenal of any serious practicing engineer or scientist. Modern packages though are a nice compact up-to-date tools collection.

автор: N N

Jun 17, 2019

Great background information on best-practice R usage.