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

4.5
5,083 Bewertungen
964 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: https://flic.kr/p/gdMuhT...
Highlights
Well taught
(48 Bewertungen)
Basic course
(76 Bewertungen)

Top-Bewertungen

SJ

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.

JM

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,

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876 - 900 von 932 Bewertungen für A Crash Course in Data Science

von 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.

von 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.

von nitesh w

Jul 10, 2017

It was very basic, could have covered more

von Gregory G K

May 09, 2016

Interesting but uneven. Felt like the first draft of a team-taught course

von 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)

von Clive G C

Dec 31, 2017

An excellent high level overview, the presentations were strong. Could be more hands-on.

von Andrew

Sep 11, 2017

Beyond elementary in my opinion.

von Rubén D C R

Sep 25, 2016

Excersices! real excersices to really understand the theory.

von C.J. d W

Feb 16, 2016

Very basic level, nice talks though

von Chow T W

Oct 28, 2015

Simple and good overview of Data Science

von 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.

von Joey S

Feb 23, 2018

Pretty General, Not many interesting points.

von Aarti K

Jan 03, 2018

The teachers spoke really fast with which it became difficult to grasp the words. Overall it was good.

von 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.

von Paul L

Jan 29, 2018

B

von 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.

von Jimmy H J G

Sep 17, 2018

this is Old content

von 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.

von Peter L

Jul 25, 2018

Added value is highly dependent of your experience with data analysis or data engeneering

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 ciri

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