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Kursteilnehmer-Bewertung und -Feedback für Data Science in Real Life von Johns Hopkins University

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1,987 Bewertungen
233 Bewertungen

Über den Kurs

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses. This is a focused course designed to rapidly get you up to speed on doing data science in real life. 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 how to: 1, Describe the “perfect” data science experience 2. Identify strengths and weaknesses in experimental designs 3. Describe possible pitfalls when pulling / assembling data and learn solutions for managing data pulls. 4. Challenge statistical modeling assumptions and drive feedback to data analysts 5. Describe common pitfalls in communicating data analyses 6. Get a glimpse into a day in the life of a data analysis manager. The course will be taught at a conceptual level for active managers of data scientists and statisticians. Some key concepts being discussed include: 1. Experimental design, randomization, A/B testing 2. Causal inference, counterfactuals, 3. Strategies for managing data quality. 4. Bias and confounding 5. Contrasting machine learning versus classical statistical inference Course promo: https://www.youtube.com/watch?v=9BIYmw5wnBI Course cover image by Jonathan Gross. Creative Commons BY-ND https://flic.kr/p/q1vudb...
Highlights
Statistics review
(44 Bewertungen)

Top-Bewertungen

SM

Aug 20, 2017

A very good and concise course that helps to understand the basics of the Data Science and its applications. The examples are very relevant and helps to understand the topic easily.

ES

Nov 12, 2017

Highly educational course on the realities of data analysis. Many good tips for your own analyses as well as for managing others responsible for coherent and accurate analyses.

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201 - 225 von 234 Bewertungen für Data Science in Real Life

von Giovany G J

Jul 15, 2020

I would prefer that the examples be expressed with statistical and mathematical calculations

von Gilson F

Aug 02, 2019

Não gostei muito da didatica do instrutor e os slides não ajudam no entendimento

von emilio z

Jun 06, 2017

Explanations in videos qere not very clear nor very well connecetd with the Quiz

von Christopher L

May 03, 2018

Would have liked a bit more examples and math in some cases. Others were fine.

von Ioannis L

Apr 09, 2017

A bit less engaging than the other parts of the Executive Data Science course.

von Patricia S

Jan 02, 2020

good content but could be simplified and presented in a more focused man

von Gowtham V

May 03, 2020

Would like to have simpler examples to understand some of the concepts.

von Amal L C

Mar 16, 2017

It was quite hard with all the statistical jargon. Too much theory.

von Poon F

Jan 30, 2018

This class has more useful materials than previous ones.

von Manas B

May 11, 2016

Relevant materials, but lecture delivery is rather dry,

von Matej K

May 01, 2018

Sometimes it was hard to understand what's going on.

von Angelina

Apr 02, 2019

The material is too long and boring.

von Weihua W

Jan 19, 2016

Too short, too expensive.

von Tamara G

Jun 07, 2020

Technical vocabulary

von Yuvaraj B

Dec 26, 2017

Very Good Content

von Mohammed R

Aug 05, 2020

Good

von Jason C

Nov 06, 2018

I found this course to be notably worse than all of the others in the series. There is very very little practical content provided within the lectures. Way too many summaries or over-views of what's to come next without really getting into the nuances of what is discussed as a course topic. Way too much repetition of the exact same content, there is even repetition of content in this course that was presented in another one of the courses in the series. Many of the examples are purely meant as a comedic aside rather than actually functioning to discuss the topic with depth. E.g. - talking about statistical modeling and putting up a picture of Ben Stiller from Zoolander - then keeping the picture up there for the entire explanation. There's literally a Nic Cage example provided for the confounding factor lecture only for the instructor to say directly after "This isn't actually the best example" - then proceeds to not explain why it was brought up aside from mentioning there's a spurious correlation. Way too much repetition of similar examples - showing photos of a muscular v. skinny Christian Bale. This pop-culturey reference isn't needed in the first place and doesn't need to be shown in triplicate. I don't mind repetition if there is additional nuance or content provided through them, but that isn't the case in this course. I find there is too much focus on side tangents, where the instructor seems to change thoughts mid-sentence but forgets to come back to the original idea. I think that every single video could be cut down by 25%, purely by being more concise, and should include more nuanced descriptions. I found it particularly odd that instrumental variables were noted as a rather clever technique, yet an explanation was intentionally avoided, however an example was still provided. Bringing up a topic, intentionally refusing to define it, then providing an example directly after just doesn't make sense. I think that more time needs to be spent refining the lectures so that they're designed to teach content. It has the feel of someone who's talking about a field to get people interested in it rather than a practical training course. Many key terms are very poorly defined with examples (on many cases the audience is referred to wikipedia for explanations) in which the basics are repetitively explained while the nuances are glossed over. There seems to be an odd theme where summaries and over-generalizations are far too frequent and yet the key terms and how they relate to examples are an afterthought. I don't think the summaries are necessary given the fact that users can literally re-watch every single video and there isn't enough total content to justify a summary in the first place. Additionally, this course also seems to deviate from the others in that there is an assumption that the student has a heavy amount of programming experience already built in (or that's my assumption since many of the term explanations aren't discussed too heavily). Prior lectures break down the basics more and indicate that potential managers should pursue the data specialization courses.

von Francisco

Jun 21, 2020

The lecturer seems afraid of the camera and the feedback on the quizzes should be better. Also, the summary readings should have all the information in the presentations, so you can check up everything more easily in one place.

von Aline N O

Jul 17, 2019

This course for me was the most difficult to understand. Using as example situations with health area was hard to understand how I can apply in my case. But in general, the other courses were very nice for me.

von Jean-Gabriel P

Aug 10, 2017

OK content but delivery could be better. Also poor value for money (you pay 49$ for a course you can finish in a few days) versus other Coursera courses that get you much more bang for your buck.

von UMUT R A

Jun 20, 2020

worst course in executive data science specialization, hard to understand concept. specific examples on health researchs are not common to understand

von Karun T

Feb 28, 2017

The content was redundant at times, at other the dots that were trying to be connected were to wide apart on the spectrum

von Marcelo H G

Jul 29, 2017

It is good but demands statistics and some knowledge in research area.

von Julià D A

Jun 13, 2017

Too qualitative, I would had liked some hands-on examples.

von Shafeeq S

Jan 08, 2019

Not that engaging content.Too much theoretical approach.