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

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2,139 Bewertungen
252 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
19. Aug. 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
11. Nov. 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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126 - 150 von 252 Bewertungen für Data Science in Real Life

von David G

14. Dez. 2016

Nice course thx

von Manish K

4. Juli 2020

Nice course :)

von Kayal V

8. Juni 2020

Really Awesome

von Gerwin N B

18. Aug. 2020

Great course!

von Ng T C

27. Jan. 2019

Good learning

von Mario L

6. Mai 2018

Challenging

von ellen w

6. Aug. 2017

Excellent!

von Pablo A L

8. Feb. 2016

Marvelous!

von Flt L G R

22. Juli 2020

THANKS...

von DR. S T C

14. Juli 2020

Excellent

von Mohammad S H S

19. Juni 2020

Thank you

von Wladimir R

30. Sep. 2018

Excellent

von Ahmed T

24. Apr. 2017

Excellent

von Reiner P

30. Mai 2020

Perfect

von David C

7. Mai 2020

awesome

von Chander W

10. Nov. 2019

Amazing

von Hector R C C

17. März 2019

thanks!

von Bauyrzhan S

13. Juni 2018

Perfect

von HOSSAM E D M S A

2. Dez. 2020

Thanks

von Mathew G

16. Aug. 2020

great

von DR. M E

27. Apr. 2020

good

von ALAA A A

11. Jan. 2018

good

von Dr V K S G

21. Juli 2020

OK

von Augustina R

29. Dez. 2016

Some of the material here was repeated from other courses but overall I felt this was my favorite course in the series. I particularly appreciated the real life examples of what can go wrong with data collection and suggestions/best practices for how to handle that. It gave me a lot of ideas for how to deal with some uncertainties I was facing in some of my own research.

von Clifton d L

6. Dez. 2017

Great that the messy reality is acknowledged and not only the perfect theoretical data science is explained, but also the things that usually go wrong (and how to mitigate these issues).

Some of the quiz with "check multiple answers" didn't seem clear to me / I found opinionated.