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

4.4
1,547 ratings
185 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 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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126 - 150 of 184 Reviews for Data Science in Real Life

By Debasish M

Feb 02, 2017

Practical approach and gotchas to consider for doing data science in real life

By Rorie D

Apr 20, 2016

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

By Jomo C

Jan 28, 2018

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

By Natalya K

Jul 08, 2017

A bit difficult to understand compared with other course of the specialization, but useful

By Karthik S N

May 01, 2016

Good concepts - apply to anyone new to data science.

Lot of good 'read further' links and materials. Learnt a lot.

By Angel S

Jan 17, 2016

Pretty useful course

By Suman C

Mar 05, 2018

Expected few more real life examples and hope to see some basics of Formal modelling. Found myself lacking in understanding the formal modelling concepts and how to arrive at the formulas.

Other than that the course helped me to get started in Data Science.

By Brian N

Apr 11, 2018

Good for introduction in Data Science Process

By SATISH R

Jun 07, 2017

Great

By Deepa F P

Sep 05, 2017

Good content

By Chris C

Nov 22, 2017

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

By JOSEPH A

May 09, 2018

Good course - I'm now confident to oversee an end-to-end data science experiment. Some interactivity would make this the perfect overview of data science.

By Jeffery T

Dec 01, 2017

Good course for managers

By Rui R

Jun 18, 2017

Too much theory ...

By Nishant J

Mar 05, 2018

Examples used in this course are related to Lifescience and candidates like me find it difficult to correlate. It would be beneficial to use some common life examples.

By Venuprasad R

Jan 05, 2016

Very practical views

By Hubertus H

Jan 27, 2017

Good summary on experimental design.

By Augustina R

Dec 30, 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.

By Clifton d L

Dec 06, 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.

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 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 Angelina

Apr 02, 2019

The material is too long and boring.

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 Daniel C d F

Dec 06, 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 Cauri J

Jul 04, 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.