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

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2,088 Bewertungen
249 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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176 - 200 von 248 Bewertungen für Data Science in Real Life

von Chris C

Nov 22, 2017

A little difficult overall but had some key points to take away.

von Jomo C

Jan 28, 2018

Good course, Longer than expected. Very satisfying at the end

von Rorie D

Apr 20, 2016

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

von Navneet W

Sep 10, 2020

On of the best courses of Data science on Coursera.

von Brian N

Apr 11, 2018

Good for introduction in Data Science Process

von Paul C

Nov 04, 2016

A solid course with lots of practical advice.

von Paulose B

Oct 31, 2016

Short session need more handson excercise

von JERRY O

Jan 22, 2020

Good course with vibrant instructors.

von SANTOSH K R

Jan 07, 2017

More real world examples are required

von Hubertus H

Jan 27, 2017

Good summary on experimental design.

von Nachum S

Jul 13, 2018

Good, a bit long for the material.

von Setia B

Dec 07, 2017

I really enjoyed the course :)

von Jeffery T

Dec 01, 2017

Good course for managers

von Angel S

Jan 17, 2016

Pretty useful course

von Venuprasad R

Jan 05, 2016

Very practical views

von Rui R

Jun 18, 2017

Too much theory ...

von SARAVANAN.V

Jun 20, 2020

Nice course 👍

von Deepa F P

Sep 05, 2017

Good content

von SARMAD H

Aug 05, 2020

Nice course

von R.K.Suriyakumar

Jun 07, 2020

its good

von Ramanathan

Jul 13, 2020

Nothing

von SATISH R

Jun 07, 2017

Great

von David T

Nov 14, 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

Aug 17, 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

Dec 07, 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.