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?): | 11 |
The Three V’s: | 11 |
Retail and Big Data - How It’s Used: | 11 |
Big Data, Definition and Concerns: | 11 |
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: | 12 |
Four Question Framework for Visualization Process: | 12 |
Key Takeaways: | 12 |
Unit 5_Summary V2 | 13 |
Unit 5: Using Linear Regression; Data Analysis | 13 |
Purpose of Analysis: | 13 |
Types of Data Analysis; Linear Regression: | 13 |
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: | 14 |
First Steps: | 14 |
Example: Hand hygiene compliance rate in a hospital. | 14 |
Availability Bias: | 14 |
Counterfactual Scenario: | 14 |
Key Takeaways: | 14 |
Made with FlippingBook - Online catalogs