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

4.5
Оценки: 5,156
Рецензии: 977

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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901–925 из 946 отзывов о курсе A Crash Course in Data Science

автор: Andrew

Sep 11, 2017

Beyond elementary in my opinion.

автор: Yi P C

Aug 19, 2017

not bad but not good enough for showing examples like data visualization and how to build the mind of data science for several fields (finance, marketing, sales and so on)

автор: Rubén D C R

Sep 25, 2016

Excersices! real excersices to really understand the theory.

автор: Dave W

Jul 03, 2017

Pretty basic course. Good if you are completely new to the space. There are good references to tools for further investigation.

автор: Hason G

Aug 18, 2017

Would have liked to seen more examples from other industries for those not in health industry

автор: Moein Z B

Aug 17, 2018

For managers and who like to know what is data science in general

автор: Peter L

Jul 25, 2018

Added value is highly dependent of your experience with data analysis or data engeneering

автор: Jason M

Jul 13, 2018

Pretty high level and quick. Hoping the remaining courses in the specialization give more depth. (Completed this course in an afternoon.)

автор: Saurabh G

Aug 12, 2019

Not all lectures in the course are well done. The one on the data scientist toolbox is good and could have more details. The one separating data science from statistics is too confusing. May need to redo the video on that one.

автор: Magne G

Sep 19, 2019

Okay content, very mix of level of information. Could state better the terms used in the DS world. The quiz part is not well formed questions, more there to mislead than actuelly verify the knowledge

автор: Jouke A M

Dec 07, 2018

Not very complete, also you need some knowledge of the field already otherwise you will be left in the dark at certain moments. Not a very consistent course. I expected better

автор: Shafeeq S

Jan 08, 2019

Not that engaging content. bit lengthy

автор: ciri

Mar 04, 2019

Came in with high expectations, but the content didn't meet them. Some of the videos have poor audio/video quality, read out dry definitions that are not very relevant. The lecture notes and video content contain factual mistakes (section of software is filled with errors) and confuse the notion of machine learning with data science throughout.

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.

автор: Jose C C

Oct 05, 2015

This course is too short.

автор: Boris L

Oct 05, 2015

Very shallow

автор: Deepak G

Jun 28, 2016

Very short. Quality of the course is also not that good.

автор: Brandon L

Aug 01, 2016

Good intent but poor execution. Tries to summarize all the major topics but ends up delivering a totally disjointed, cut-and-paste experience with no real flow.

автор: Prashant P

Dec 22, 2015

Too theoretical, e.g, comparison between statistics and ML is not at all useful. Too many quizzes after very short classes and on topics of absolutely generic things.

автор: Nellai S

Jul 02, 2017

At some places, one lesson had the text and the next lesson was redundant with part of the information on video. you could club them in one an

автор: iair l

Dec 26, 2015

too basic, the 4 courses of this specialization could be just one course.

автор: Eduardo R L

Oct 07, 2016

1-week does not seem enough for a Crash Course

автор: Marcelo H G

Jul 12, 2017

Too much Superficial. Too fewer quizes. More external videos about hadoop, python, spark, data lakes. More paradigms broken. Need to explain what is On premise, rent and cloud.

автор: Yousuf B

Mar 11, 2017

Overly academic

автор: Sukumar N

Apr 20, 2016

Ref: "A Crash Course in Data Science" the content could be presented in a simpler way. Some of the presentations sounds little vague and conceptual level like an Advanced Math or, Statistics class. I am wondering since this is an Executive program, is there a simpler and easy to grasp way to present the material. The text download files (i.e. txt) document descriptions needs to be more clearer. The Power Point downloads are excellent and are to the point.