(76 avaliações)
(48 avaliações)
SJ
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.
MD
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!
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
por REKIL P
•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
Nice!
por Tristan C
•18 de mai de 2020
Ok
por Paul L
•29 de jan de 2018
B
por Seeneth H
•19 de nov de 2017
-
por aman
•27 de set de 2016
O
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.