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.
Data Science in Real Life
This course is part of Executive Data Science Specialization
Instructors: Brian Caffo, PhD
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What you'll learn
Identify strengths and weaknesses in experimental designs
Learn novel solutions for managing data pulls
Describe common pitfalls in communicating data analyses
Understand a typical day in the life of a data analysis manager
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There is 1 module in this course
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.
What's included
22 videos10 readings6 assignments
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Johns Hopkins University
Johns Hopkins University
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