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Learner Reviews & Feedback for Data Science in Real Life by Johns Hopkins University

4.4
stars
2,134 ratings
252 reviews

About the Course

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 Reviews)

Top reviews

SM
Aug 19, 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 11, 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 of 251 Reviews for Data Science in Real Life

By 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.

By 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.

By Rajeev R

Dec 7, 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.

By Gonzalo G A

Dec 16, 2016

It's sometimes difficult to follow professors beacuse they take for granted information about the examples they use that is not evident for the learners. They should take a minute to explain a little bit more what the examples consist of and what are the charts they show. As it happens when Brian Caffo explains the blocking adjustments part.

By Cauri J

Jul 4, 2017

I found this course used a lot of jargon without explanation. It seems like the instructor understands the content so well that he assumes a level of knowledge from students that do not match the expectations of the rest of the content in this track. At the same time I found the content well presented.

By Michail C

Jul 17, 2019

This course is an excellent effort to document the issues faced in real-life data science. However, the flow of the videos seems to be a bit confusing and some of the content is explained in a weird manner.

By Daniel C d F

Dec 5, 2016

I missed several concepts to better understand some of the discussions and explanations. It was valid, but I think the statistics background should be better explored.

By Peter L

Aug 14, 2018

The course is valuable but highly focussed on scientific applications (inference) and less on business application (i.e. prediction). I hoped for a more even mix.

By Astolfo

Jul 5, 2020

It was good, but the content is harder to understand in this course.

I would prefer a similar format and emphasis as the other two last courses.

By Sean H

Nov 24, 2015

The video quality and content were good. Unfortunately, there were a lot of spelling errors and grammatical mistakes in the written portions.

By Chong K M

Mar 18, 2018

Very difficult and time consuming course which contains a lot of technical words and jargon. Not recommended for the average beginner.

By Jean-Michel M

Feb 22, 2019

I would drop some of the cartoons. They are funny but they seem to distract Bryan and overall it's distracting for us students too.

By PAVITHRA.T

Jul 28, 2020

First of all it's too tough to understand but day by day I understood something I got it ..tq.it is very helpful for my studies

By Rong-Rong C

Dec 14, 2017

There is a lot of technical jargon covered which made the course more challenging than the other courses in the series.

By Alberto M B

Mar 20, 2019

It wasn't as focus on Managing Data Scientists as I was expecting, but rather focus on tips for Data Scientist.

By Marco A P

Jan 2, 2017

Much theorical with few examples. Could incorporate examples outside the health world as well.

By Giovany G J

Jul 15, 2020

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

By Gilson F

Aug 2, 2019

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

By emilio z

Jun 6, 2017

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

By Christopher L

May 3, 2018

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

By Ioannis L

Apr 9, 2017

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

By Patricia S

Jan 2, 2020

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

By Gowtham V

May 2, 2020

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

By Amal L C

Mar 16, 2017

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

By Poon F

Jan 30, 2018

This class has more useful materials than previous ones.