Data Science in Real Life
Johns Hopkins University via Coursera
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Overview
Class Central Tips
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
Syllabus
- Introduction, the perfect data science experience
- This course is one module, intended to be taken in one week. Please do the course roughly in the order presented. Each lecture has reading and videos. Except for the introductory lecture, every lecture has a 5 question quiz; get 4 out of 5 or better on the quiz.
Taught by
Jeff Leek, Brian Caffo and Roger Peng
Tags
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Reviews
3.2 rating, based on 11 reviews
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Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be easy.
Data Science in Real Life is the fourth and final course in the “Executive Data Science” specialization offered by John Hopkins University on Coursera. The one-week course examines various steps in the data analysis process and contrasts ideal outcomes... -
I enjoyed taking this course and I think that it delivers on what it promises on the title. I recommend taking this course after the first three recommended titles in the Coursera Specialization where it belongs.
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Steven Meyer completed this course.
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Ali Taheri completed this course.
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Anonymous completed this course.
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Shaun Moate completed this course.
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Colin Khein completed this course.
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Janet Wesner completed this course.
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Alex Ivanov completed this course.
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Micah Hall completed this course.
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Pawel Krzysztofik completed this course.