Data Science in Real Life free videos and free material uploaded by Johns Hopkins University Staff .
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
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
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