Introduction, the perfect data science experience-What you've gotten yourself into
Just for fun, course promotional video
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Pre-Course Survey
Course structure
Grading
Data science in the ideal versus real life Part 1
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Data science in the ideal versus real life Part 2
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Examples
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Machine Learning vs. Traditional Statistics Part 1
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Machine Learning vs. Traditional Statistics Part 2
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Introduction, the perfect data science experience-The data pull is clean
The data pull is clean
Managing the Data Pull
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Introduction, the perfect data science experience-The experiment is carefully designed, principles
The experiment is carefully designed
Experimental design and observational analysis
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Causality part 1
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Causality Part 2
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What Can Go Wrong?: Confounding
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Introduction, the perfect data science experience-The experiment is carefully designed, things to do
The experiment is carefully designed, things to do
A/B Testing
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Sampling bias and random sampling
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Blocking and adjustment
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Introduction, the perfect data science experience-Results of analyses are clear
Results of analyses are clear
Multiplicity
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Effect size, significance, & modeling
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Comparison with benchmark effects
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Negative controls
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Introduction, the perfect data science experience-The Decision is obvious
The decision is obvious
Non-significance
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Estimation Target is Relevant
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Introduction, the perfect data science experience-The analysis product is awesome
The analysis product is awesome
Report writing
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Version control
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Introduction, the perfect data science experience-Post-Course Survey
Post-Course Survey