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Data Science in Real Life, Johns Hopkins University

4.4
1,491 ratings
175 reviews

About this 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

By 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.

By 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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174 Reviews

By Omid Faseli

May 10, 2019

Thank you very much for your excellent course.

Best Regards

Omid Faseli

By Georgios Papadopoulos

May 06, 2019

Very good introductory topics!

By Priyanka Frances Pereira

Apr 14, 2019

Excellent technical information!

By Gustavo Villa

Apr 14, 2019

Help me understand what can I expect from a real data science project.

By pietbartolo

Apr 12, 2019

Very useful course! I really enjoyed the technical not so much the statistical part of the course.

By Angelina

Apr 02, 2019

The material is too long and boring.

By Alberto Molino Benito

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 Jason Goungo

Mar 18, 2019

Very informative and a good introduction into the aspects faced while doing Data Science!

By Hector Raul Colonia Coral

Mar 17, 2019

thanks!

By BAHAR AĞMA

Feb 24, 2019

It is a helpful course about a statistical area. I recommend it.