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.
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,
por rahul suresh•
Mar 19, 2019
not worth it
por Zaw_Tun Naing•
Mar 19, 2019
I was very delighted to have learned that course, I believe that I really gained a lot of insights into the subject although my career is not a data scientist.
por Mauricio Flores Rios•
Mar 17, 2019
Very superficial, highly disappointed
por Nnene Ema•
Mar 17, 2019
Everyone who wants to study Data Science should first take this course. It gives in-depth orientation to the field of data science.
por Julián Daniel Jiménez Krause•
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.
por Inaam Ur Rehman•
Mar 15, 2019
Its a good course of study to dive in the data science word. To know about the tools and techniques for a data analyst and data scientist.
Mar 14, 2019
Nice and well taught .There is really alot to learn
por Hector Raul Colonia Coral•
Mar 13, 2019
Excelente curso introductorio!
por Leslie Teo•
Mar 05, 2019
The material and lectures are good but the quizes are not very helpful and somewhat random (in answers). The small number of questions make them very unforgiving.
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.