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,
von 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.
von 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
von Shafeeq S•
Jan 08, 2019
Not that engaging content. bit lengthy
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.
von 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.
von 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
von 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.
von Hussain, C•
Oct 21, 2015
Very general course. Doesn't give much insight into data science.
von Mohsin Q•
Oct 31, 2016
They could have stated the audience of the course more clearly. I found most of the information irrelevant that added little value. Most of the things discussed are generic and would apply to any project.
von Boris L•
Oct 05, 2015
von Yousuf B•
Mar 11, 2017
von 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.
von Jose C C•
Oct 05, 2015
This course is too short.
von Deepak G•
Jun 28, 2016
Very short. Quality of the course is also not that good.
von 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.
von iair l•
Dec 26, 2015
too basic, the 4 courses of this specialization could be just one course.
von Eduardo R L•
Oct 07, 2016
1-week does not seem enough for a Crash Course
von 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.
von Arno B•
May 04, 2017
very elementary. Takes approximately 2 hours to complete.
cannot continue with the in-dept material but have to wait until next week (and payment ofcourse).
von 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
von Robin S•
Jun 13, 2019
The only reason this course is two stars is because the content could be useful to a beginner in the field. The course itself, however, is of poor quality with un-engaging video content and an unedited book with multiple sections that are clearly derived verbatim from the sub-par video lectures. It could be drastically improved with a little effort and would hopefully provide more value to learners with genuine interest.
von Armughan A•
Oct 16, 2019
Boring and useless course.....
von rahul s•
Mar 19, 2019
not worth it
von Miroslav K•
Sep 09, 2016
von Alessandro V•
Apr 22, 2016
It's too short, I think it should be a part of a course and not a course itself.
It is a repetition of concepts and examples from other courses by john hopkins univ.