Chevron Left
Voltar para Um curso intensivo em ciência dos dados

Comentários e feedback de alunos de Um curso intensivo em ciência dos dados da instituição Universidade Johns Hopkins

8,022 classificações

Sobre o curso

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:
Basic course

(76 avaliações)

Well taught

(48 avaliações)

Melhores avaliações


9 de set de 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.


27 de ago de 2016

Is really hard to summarize the potential of Data Science and being clear, but I think that the instructors have done their best, so that we can achieve the most from the Course.

Great Job!

Filtrar por:

1401 — 1425 de 1,473 Avaliações para o Um curso intensivo em ciência dos dados

por C.J. d W

16 de fev de 2016

Very basic level, nice talks though

por Andrew

11 de set de 2017

Beyond elementary in my opinion.

por James E M

30 de out de 2020

The course needs better notes.

por tanmay p

20 de jan de 2018

useful basics for data science

por Dr.Palaniappan S

9 de abr de 2020

Practical example are needed


14 de fev de 2018

Good for ABSOLUTE beginners

por Zhao Z (

2 de mai de 2021

Quiz is not designed good.

por Harsh D

21 de jun de 2020

Its ok , waiting for more

por Riaan R

20 de fev de 2019

Very basic and to short.

por Pushpendra S

11 de fev de 2020

Too shallow in coverage

por Jimmy H J G

17 de set de 2018

this is Old content

por Camilo C

10 de out de 2016

Very basic course!

por Angel S

11 de jan de 2016

Interesting course

por Yuvaraj B

26 de dez de 2017

Very Good Content

por Thomas N

3 de jan de 2017

needed more depth

por Víctor E G P

28 de dez de 2017

Good to know

por Sergio A M

1 de set de 2017

Very basic.

por Vladimir C

23 de mai de 2016

Too basic.

por sandeep d

16 de set de 2020

too easy

por Mohamed T K

27 de jun de 2020


por Tristan C

18 de mai de 2020


por Paul L

29 de jan de 2018


por Seeneth H

19 de nov de 2017


por aman

27 de set de 2016


por Julián D J K

16 de mar de 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.