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

2,262 Bewertungen
272 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: Course cover image by Jonathan Gross. Creative Commons BY-ND
Statistics review
(44 Bewertungen)


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.

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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201 - 225 von 274 Bewertungen für Data Science in Real Life

von Rorie D

20. Apr. 2016

great approach, thanks. A few typos, but otherwise great.

von Navneet W

10. Sep. 2020

On of the best courses of Data science on Coursera.

von Brian N

10. Apr. 2018

Good for introduction in Data Science Process

von Paul C

4. Nov. 2016

A solid course with lots of practical advice.

von Paulose B P

31. Okt. 2016

Short session need more handson excercise


22. Jan. 2020

Good course with vibrant instructors.


7. Jan. 2017

More real world examples are required

von Hubertus H

27. Jan. 2017

Good summary on experimental design.

von Nachum S

13. Juli 2018

Good, a bit long for the material.

von Setia B

6. Dez. 2017

I really enjoyed the course :)

von Jeffery T

30. Nov. 2017

Good course for managers

von Angel S

17. Jan. 2016

Pretty useful course

von Venuprasad R

5. Jan. 2016

Very practical views

von Rui R

18. Juni 2017

Too much theory ...


20. Juni 2020

Nice course 👍

von Deepa F P

5. Sep. 2017

Good content


5. Aug. 2020

Nice course

von R.K.Suriyakumar

7. Juni 2020

its good

von ECE- R G

13. Juli 2020



7. Juni 2017


von Luis A S E

15. März 2021


von Parag

7. Feb. 2021


von David T

14. Nov. 2016

Some good tips, nothing terribly new for those who have had a course in statistics. Materials made easy to digest. The variety from the 3 instructors was nice. Missed opportunity: to combine the best aspects from each. The course notes were either excerpts from R.Peng's books /blogs (good) or automated transcripts (complete with typical AI typos... "wait" instead of "weight"). Some lectures were repetitive from one course to another. Slides with examples were useful, slides with clip-art and comic stips less so. Tries to be something for everyone. Would be better to aim either at former DS analysts aspiring to be managers or seasoned managers trying to better understand DS.

von Ruben S

17. Aug. 2016

Brian tries to achieve too much in too little time. It addresses important issues and it gives a good overview, including some hidden gems (Machine Learning vs Stats, for example), but it feels mostly too rushed and superficial for my taste/expectations, and it fails to connect to my previous knowledge (and I have a PhD in Maths, although no strong Stats background), hence little added value for me when I cannot relate to what is being discussed.

von Rajeev R

7. Dez. 2015

Lectures themselves were OK, but presentation needs work. Intro session was very repetitive. Lot of jargon introduced without explanation. Pop-ups w text showed up but disappeared before I was able to finish reading them. Best part of course was actually the text notes at the beginning of each sesssion. A minor nitpick: course description suggests that there are 3 instructors presenting, but I only saw one.