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 |

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