Vulcan - Leading with Analytics: Program Resources


Unit 1_Summary V2
2

Unit 1: Why Analytics Matter
2

Introduction:
2

Definitions of Analytics:
2

Types of Analytics:
2

Key Takeaways:
2

Unit 2_Summary V2
6

Unit 2: Asking Connecting Questions
6

The Successful Data Scientist:
6

Connecting questions equal “crunchy questions”* and should:
6

Model Building:
6

Connecting Questions Framework:
6

Key Takeaways:
6

Unit 3_Summary V2
11

Unit 3: Data Acquisition, Quality and Strategy
11

Types of Data Sources (Where Does Data Come from?):
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The Three V’s:
11

Retail and Big Data - How It’s Used:
11

Big Data, Definition and Concerns:
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Data Quality Concerns:
11

Five Stages of Analytical Development:
11

Key Takeaways:
11

Unit 4_Summary V2
12

Unit 4: Visualizing Data
12

Examples of Data Visualization:
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Four Question Framework for Visualization Process:
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Key Takeaways:
12

Unit 5_Summary V2
13

Unit 5: Using Linear Regression; Data Analysis
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Purpose of Analysis:
13

Types of Data Analysis; Linear Regression:
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Hypothesis Testing: Assessing whether an observed difference is a fluke or real; also referred to as statistical significance. Is the relationship significant or a fluke?
13

Unit 6_Summary V2
14

Unit 6: Putting It All Together
14

Making and Implementing Decisions:
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First Steps:
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Example: Hand hygiene compliance rate in a hospital.
14

Availability Bias:
14

Counterfactual Scenario:
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Key Takeaways:
14
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