(76 Reviews)
(48 Reviews)
SJ
Sep 9, 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
Aug 27, 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!
By tanmay p
•Jan 20, 2018
useful basics for data science
By Dr.Palaniappan S
•Apr 9, 2020
Practical example are needed
By Rekil P
•Feb 14, 2018
Good for ABSOLUTE beginners
By Zhao Z (
•May 2, 2021
Quiz is not designed good.
By Harsh D
•Jun 21, 2020
Its ok , waiting for more
By Riaan R
•Feb 20, 2019
Very basic and to short.
By Pushpendra S
•Feb 11, 2020
Too shallow in coverage
By Jimmy H J G
•Sep 17, 2018
this is Old content
By Camilo C
•Oct 10, 2016
Very basic course!
By Angel S
•Jan 11, 2016
Interesting course
By Yuvaraj B
•Dec 26, 2017
Very Good Content
By Thomas N
•Jan 3, 2017
needed more depth
By VÃctor E G P
•Dec 28, 2017
Good to know
By Sergio A M
•Sep 1, 2017
Very basic.
By Vladimir C
•May 23, 2016
Too basic.
By sandeep d
•Sep 16, 2020
too easy
By Mohamed T K
•Jun 27, 2020
Nice!
By Tristan C
•May 18, 2020
Ok
By Paul L
•Jan 29, 2018
B
By Seeneth H
•Nov 19, 2017
-
By aman
•Sep 27, 2016
O
By 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.
By 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.
By Ryan M
•Mar 31, 2021
I felt that the speakers used an awful lot of words to say very few things - they could reduce the length of this course by about 50% if they were more concise and too the point. Also, it would help if they had microphones as it would improve the sound quality. They should also tidy up the background, e.g. wipe unrelated text from the chalkboard, and remove clutter from behind them.
By ciri
•Mar 4, 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.