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Kursteilnehmer-Bewertung und -Feedback für A Crash Course in Data Science von Johns Hopkins University

7,129 Bewertungen
1,354 Bewertungen

Über den Kurs

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials. This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA:
Basic course
(76 Bewertungen)
Well taught
(48 Bewertungen)


27. Aug. 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.\n\nGreat Job!

9. Sep. 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.

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1276 - 1300 von 1,321 Bewertungen für A Crash Course in Data Science

von Tristan C

18. Mai 2020


von Paul L

29. Jan. 2018


von Seeneth H

19. Nov. 2017


von aman

27. Sep. 2016


von Julián D J K

16. März 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 Sukumar N

20. Apr. 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 ciri

4. März 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 Mohsin Q

31. Okt. 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 Marcelo H G

12. Juli 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 Jouke A M

7. Dez. 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

22. Dez. 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 Brandon L

1. Aug. 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 Arno B

4. Mai 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

2. Juli 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 Abdullah A M

4. Okt. 2020

The teacher knows quite well, but not suitable for beginners. This course is for someone who is already an expert in this field.


4. Juli 2020

This course was good but the way of information and there should me more lectures and more detail study notes for this

von Dhruv W

10. Aug. 2020

The least interactive course to throw money at. The teachers used very conventional and un-interactive ways to teach.

von iair l

26. Dez. 2015

too basic, the 4 courses of this specialization could be just one course.

von Pulkit N

6. Apr. 2020

Was a very broad review than was expecting.Content covered is too less

von Hussain, C

21. Okt. 2015

Very general course. Doesn't give much insight into data science.

von Deepak G

28. Juni 2016

Very short. Quality of the course is also not that good.

von Eduardo R L

7. Okt. 2016

1-week does not seem enough for a Crash Course

von Shafeeq S

8. Jan. 2019

Not that engaging content. bit lengthy

von Jose C C

5. Okt. 2015

This course is too short.

von Yousuf B

10. März 2017

Overly academic