Managing Data Analysis-Introduction
What this Course is About
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Pre-Course Survey
Course Textbook: The Art of Data Science
Conversations on Data Science
Data Science as Art
Managing Data Analysis-The Data Analysis Iteration
Data Analysis Iteration
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Stages of Data Analysis
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Epicycles of Analysis
Managing Data Analysis-Six Types of Questions
Six Types of Questions
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Six Types of Questions
Managing Data Analysis-Characteristics of a Good Question
Characteristics of a Good Question
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Characteristics of a Good Question
Managing Data Analysis-Exploratory Data Analysis
Exploratory Data Analysis Goals & Expectations
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EDA Check List
Managing Data Analysis-Using Models to Explore Your Data
Using Statistical Models to Explore Your Data (Part 1)
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Using Statistical Models to Explore Your Data (Part 2)
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Assessing a Distribution
Assessing Linear Relationships
Managing Data Analysis-Exploratory Data Analysis: When to Stop
Exploratory Data Analysis: When to Stop
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Exploratory Data Analysis: When Do We Stop?
Managing Data Analysis-Inference
Making Inferences from Data: Introduction
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Populations Come in Many Forms
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Inference: What Can Go Wrong
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Factors Affecting the Quality of Inference
A Note on Populations
Managing Data Analysis-Formal Modeling
General Framework
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Associational Analyses
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Prediction Analyses
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Managing Data Analysis-Inference vs. Prediction: Implications for Modeling Strategy
Inference vs. Prediction
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Inference vs. Prediction
Managing Data Analysis-Interpretation of Results
Interpreting Your Results
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Interpreting Your Results
Managing Data Analysis-Communication
Routine Communication in Data Analysis
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Making a Data Analysis Presentation
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Routine Communication
Managing Data Analysis-Post-Course Survey
Post-Course Survey