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Отзывы учащихся о курсе A Crash Course in Data Science от партнера Университет Джонса Хопкинса

4.5
звезд
Оценки: 5,516
Рецензии: 1,046

О курсе

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT...
Основные моменты
Basic course
(рецензий: 76)
Well taught
(рецензий: 48)

Лучшие рецензии

SJ

Sep 10, 2017

This is a great starter course for data science. My learning assessment is usually how well I can teach it to someone else. I know I have a better understanding now, than I did when I started.

JM

Jan 02, 2018

It is a very good course even if you are familiar with some aspects of data science work. If I have to make a suggestion, I would remark the importance of design skills during a data product,

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951–975 из 1,013 отзывов о курсе A Crash Course in Data Science

автор: Deepak R

Jan 01, 2017

Good overview... but expected more content..

автор: nitesh w

Jul 10, 2017

It was very basic, could have covered more

автор: Chow T W

Oct 28, 2015

Simple and good overview of Data Science

автор: Richard B

Jul 02, 2017

it was a little lighter than I expected

автор: Jean-Michel M

Feb 14, 2019

The trainers are not equal in quality.

автор: Rippon K S

Sep 06, 2017

I find the course to basic in nature

автор: Brian N

Jan 09, 2017

A good introduction to data science.

автор: C.J. d W

Feb 16, 2016

Very basic level, nice talks though

автор: Andrew

Sep 11, 2017

Beyond elementary in my opinion.

автор: tanmay p

Jan 21, 2018

useful basics for data science

автор: REKIL P

Feb 14, 2018

Good for ABSOLUTE beginners

автор: Riaan R

Feb 20, 2019

Very basic and to short.

автор: Pushpendra S

Feb 12, 2020

Too shallow in coverage

автор: Jimmy H J G

Sep 17, 2018

this is Old content

автор: Camilo C

Oct 10, 2016

Very basic course!

автор: Angel S

Jan 11, 2016

Interesting course

автор: Yuvaraj B

Dec 26, 2017

Very Good Content

автор: Thomas N

Jan 03, 2017

needed more depth

автор: Víctor E G P

Dec 28, 2017

Good to know

автор: Sergio A M

Sep 01, 2017

Very basic.

автор: Vladimir C

May 23, 2016

Too basic.

автор: Paul L

Jan 29, 2018

B

автор: Seeneth H

Nov 20, 2017

-

автор: aman

Sep 27, 2016

O

автор: Julián D J K

Mar 16, 2019

i was quite dissapointed from the 2nd half of the module "A Crash Course in Data Science". The most interesting part for me was right at the begining: the explanation of the differences and overlappings between ML (area where I have experience) and traditional statistics (area I've never worked in). I deeply disliked a repeated message across different videos in the 2nd half of the module, that data scientists should develop themselves all kind of software artifacts... it doesn't work like that, it cannot and must not work like that in large organisations.

I work in a large organisation. A situation that we are facing right now is that a number of data analytics initiatives are popping up like champignons across the organisation, within the different operational departments. Very often the colleagues involved are not really data scientists, often they are lawyers with an interest (and some training) in analytics, in the best case they are economists. The creation of pieces of code in every floor and corner of the organisation is a nightmare, from several points of views: security, business continuity (when one of those lawyers quits a department, often there is no one to continue / maintain that code... which by the way was written not following any standards of software development).

In that context, our management is evaluating how to put coherence and structure in all the data work, how to create synergies, share knowledge... that is the reason why I started this training (i am a middle manager; my background is mathematics MSc, i am not a data scientist / statistician though)... tempted by the title "executive data science", which I interpreted as: "how to best organise data analytics in an organisation".

In my vision of properly organising data analytics / science in a large organisation there is no space for everybody writing code, somehow, uncontroled, at each point of each data science project. Rather I would dream of a common, coherent framework, standard data quality/governance/ownership and data acquisition approach across the organisation, standard tools supporting each step of the data science project, standard methodology. If coding still needed, in particular for development of interactive websites or apps (for communication of results), then to be developed by software engineers following agile standard code development, including: analysis, prototyping, reference architecture, versioning, QA, testing, documenting...ensuring security, maintenance and continuity, ensring also reusability ...

But seems I have misunderstood the title with respect "executive". Mea culpa.