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,
автор: tanmay p•
Jan 21, 2018
useful basics for data science
автор: Aydin A•
Jun 03, 2016
Was expecting more of the how to's and a bit of programming or at least concepts of the programming/statistics, but I guess there are different interpretations of the idea of a crash course.
Definitely geared for people who work with data scientists but not in the data science field.
автор: Francisco P S•
Mar 29, 2017
The course can use more visuals instead of videos of the face of the instructor. It can also use more interactive examples as this is a more executive view instead of having scholar examples.
автор: nitesh w•
Jul 10, 2017
It was very basic, could have covered more
автор: Gregory G K•
May 09, 2016
Interesting but uneven. Felt like the first draft of a team-taught course
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: Clive G C•
Dec 31, 2017
An excellent high level overview, the presentations were strong. Could be more hands-on.
Sep 11, 2017
Beyond elementary in my opinion.
автор: Rubén D C R•
Sep 25, 2016
Excersices! real excersices to really understand the theory.
автор: C.J. d W•
Feb 16, 2016
Very basic level, nice talks though
автор: Chow T W•
Oct 28, 2015
Simple and good overview of Data Science
автор: Marco C•
Nov 16, 2015
Good course, with general and not over-detailed explanations of all the relevant topics in data Science. A good, general overview definitively worth working on.
автор: Joey S•
Feb 23, 2018
Pretty General, Not many interesting points.
автор: Aarti K•
Jan 03, 2018
The teachers spoke really fast with which it became difficult to grasp the words. Overall it was good.
автор: Daniel W•
Apr 15, 2018
I am trying to work out whether or not to get into data science, I thought this would help but still undecided.
I liked the grounding of principles, tools and methods required for the discipline.
автор: Paul L•
Jan 29, 2018
автор: Margaret K H B•
Mar 31, 2018
I felt the explanation were in between data science for beginners and someone who already had taken a statistics course. I feel that it was important at the beginning to give more real life examples of the usage of data that were compelling. And then take those real life examples and break it down for us using a single inspired project. That would have helped me better understand some of the principles that seemed a bit abstract for me.
автор: Jimmy H J G•
Sep 17, 2018
this is Old content
автор: Yousuf A•
Aug 09, 2018
A lot of the topic is described in a difficult way using unknown words(for a beginner) and with examples that I did not understand.
автор: Peter L•
Jul 25, 2018
Added value is highly dependent of your experience with data analysis or data engeneering
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
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.