Data Science and Big Data Analytics Chap 12: The Endgame, or Putting It All Together

1. Data Science and Big Data AnalyticsChap 12: The Endgame, or Putting It All Together Charles Tappert Seidenberg School of CSIS, Pace University

2. Chapter Contents • 12.1 Communicating and Operationalizing an Analytics Project • 12.2 Creating the Final Deliverables • Developing core material for multiple audiences, project goals, main findings, approach, model description, key points supported with data, model details, recommendations, tips on final presentation, providing technical specifications and code • 12.3 Data Visualization Basics • Key points supported with data, evolution of a graph, common representation methods, how to clean up a graphic, additional considerations • Summary

3. 12.1 Communicating and Operationalizing an Analytics Project

4. 12.1 Communicating and Operationalizing an Analytics ProjectDeliverables and Stakeholders

5. 12.1 Communicating and Operationalizing an Analytics ProjectDeliverables • General Deliverables – from Textbook • Presentation for Project Sponsors • Presentation for Analysts • Code • Technical Specifications • Deliverables For This Course • Presentation for Analysts – half hour per team, next week • Technical Paper for Research Day Conference • Submit CD – Presentation, Paper, Data or URL, Code

6. 12.2 Creating the Final DeliverablesCase Study – Fictional Bank Churn Prediction • This section describes a scenario of a fictional bank and a churn prediction model of its customers • The analytic plan contains components that can be used as inputs for writing the final presentations • scope • underlying assumptions • modeling techniques • initial hypotheses • and key findings

7. 12.2 Creating the Final DeliverablesCase Study – Fictional Bank Churn Prediction

8. 12.2 Creating the Final DeliverablesCase Study – Fictional Bank Analytics Plan

9. 12.2 Creating the Final Deliverables12.2.1 Developing Core Material for Multiple Audiences • Some project components have dual use • Create core materials used for both analyst and business audiences • Three areas on the next slide used for both audiences • Sections after the following overview slide • 12.2.2 – Project Goals • 12.2.3 – Key Findings • 12.2.4 – Approach • 12.2.5 – Model Description • 12.2.6 – Key Points Supported by Data • 12.2.7 – Model Details • 12.2.8 – Recommendations • 12.2.9 – Additional Tips on the Final Presentation • 12.2.10 – Providing Technical Specifications and Code

10. 12.2 Creating the Final Deliverables12.2.1 Developing Core Material for Multiple Audiences

11. 12.2 Creating the Final Deliverables12.2.2 Project Goals • The project goals portion of the final presentation is generally the same for sponsors and analysts • The project goals are described first to lay the groundwork for the solution and recommendations • Generally, the goals are agreed on early in the project • Two examples of project goals are shown next • The second example recaps the situation that motivated the project

12. 12.2 Creating the Final Deliverables12.2.2 Project Goals

13. 12.2 Creating the Final Deliverables12.2.2 Project Goals

14. 12.2 Creating the Final Deliverables12.2.3 Main Findings

15. 12.2 Creating the Final Deliverables12.2.3 Main Findings Sponsor Service Level Agreement

16. 12.2 Creating the Final Deliverables12.2.4 Approach

17. 12.2 Creating the Final Deliverables12.2.4 Approach

18. 12.2 Creating the Final Deliverables12.2.5 Model Description

19. 12.2 Creating the Final Deliverables12.2.6 Key Points Supported with Data • Identify key points based on insights and observations from the data and model results • This information lays the foundation for the coming recommendations

20. 12.2 Creating the Final Deliverables12.2.6 Key Points Supported with Data Rate of bank customers who would churn

21. 12.2 Creating the Final Deliverables12.2.7 Model Details

22. 12.2 Creating the Final Deliverables12.2.7 Model Details Caption: Model details comparing two data variables

23. 12.2 Creating the Final Deliverables12.2.8 Recommendations

24. 12.2 Creating the Final Deliverables12.2.9 Additional Tips on Final Presentation • Use imagery and visual representations • Pictures are better than words • Ensure text mutually exclusive/collectively exhaustive • Meaning: cover key points, but don’t repeat unnecessarily • Measure and quantify the benefits of the project • Requires effort to do this well • Make the project benefits clear and conspicuous • Details • Provide sufficient context for recommendations • Spell out acronyms, avoid excessive jargon

25. 12.2 Creating the Final Deliverables12.2.10 Providing Technical Specifications and Code • Deliver code plus documentation • User manual • Add extensive comments in the code • How computationally expensive to run the model? • Describe exception handling • Data outside expected data ranges • Null values • Zeros

26. 12.3 Data Visualization BasicsCommon Tools for Data Visualization

27. 12.3 Data Visualization Basics12.3.1 Key Points Supported with Data • Difficult to make key insights when data is in tables • Text shows 45 then 35 years of store operations • Ten years shown here

28. 12.3 Data Visualization Basics12.3.1 Key Points Supported with Data Shows where the BigBox store has market saturation

29. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph • Visualization can allow people to understand data on an intuitive, precognitive level • Distribution of customer (user) loyalty scores Log scale Less skewed

30. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph • Rescaled view of last figure with median ~ 2.0 • Textbook does not describe the rescaling method

31. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph • Graph of stability analysis (over time) for pricing

32. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph • Current pricing model with score distribution (rug)

33. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph • Proposed pricing model with loyalty score dist. (rug) Proposes progressively higher prices as customer loyalty increases May seem counterintuitive

34. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph Evolution of a Graph, Analyst Example

35. 12.3 Data Visualization Basics12.3.2 Evolution of a Graph Evolution of a Graph, Sponsor Example

36. 12.3 Data Visualization Basics12.3.3 Common Representation Methods

37. 12.3 Data Visualization Basics12.3.4 How to Clean Up a Graphic Example 1 Before

38. 12.3 Data Visualization Basics12.3.4 How to Clean Up a Graphic Example 1 After

39. 12.3 Data Visualization Basics12.3.4 How to Clean Up a Graphic Example 2 Before

40. 12.3 Data Visualization Basics12.3.4 How to Clean Up a Graphic Example 2 After

41. 12.3 Data Visualization Basics12.3.4 How to Clean Up a Graphic Example 2 After Alternative

42. 12.3 Data Visualization Basics12.3.5 Additional Considerations • Simple bar chart with two dimensions

43. 12.3 Data Visualization Basics12.3.5 Additional Considerations • Avoid three dimensions • Distort scales and axes, impede viewer cognition

44. Summary • Communicating the value of analytical projects is critical for sustaining the momentum of a project and building support within organizations • Deliverables to satisfy various stakeholders • Presentation for project sponsor • Presentation for analytical audience • Technical specification documents • Well-annotated production code • Best data visualizations are simple and clear