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Managing nonprofit organizations in a policy world by Vaughan, Shannon K.

Contents: “…Resource Development: Grants -- The Role of Grants in the Nonprofit Sector -- Types of Grants -- Project or Program Grants -- Operating Grants -- Seed Money -- Challenge/Matching Grants -- Government Grants and Fee-for-Service Contracts -- Distribution of Government Grants -- Public Goods and Services via Government Contracts -- Foundation Funding -- Types of Foundations -- Preparing a Grant Proposal -- Getting Started -- Budget and Project Narratives -- Submitting the Proposal -- Public Policy Consequences of Grants and Contracts -- Government Funding of Policy Priorities -- Foundation Funding to Influence Policy -- Conclusion -- Questions for Review -- Assignment -- Suggested Readings -- Web Resources -- pt. …”
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The data science handbook by Cady, Field, 1984-

Contents: “…-- 3.2 A Survey of Programming Languages for Data Science -- 3.3 Python Crash Course -- 3.4 Strings -- 3.5 Defining Functions -- 3.6 Python's Technical Libraries -- 3.7 Other Python Resources -- 3.8 Further Reading -- 3.9 Glossary -- Interlude: My Personal Toolkit -- Chapter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning -- 4.1 The Worst Dataset in the World -- 4.2 How to Identify Pathologies -- 4.3 Problems with Data Content -- 4.4 Formatting Issues -- 4.5 Example Formatting Script4.6 Regular Expressions -- 4.7 Life in the Trenches -- 4.8 Glossary -- Chapter 5 Visualizations and Simple Metrics -- 5.1 A Note on Python's Visualization Tools -- 5.2 Example Code -- 5.3 Pie Charts -- 5.4 Bar Charts -- 5.5 Histograms -- 5.6 Means, Standard Deviations, Medians, and Quantiles -- 5.7 Boxplots -- 5.8 Scatterplots -- 5.9 Scatterplots with Logarithmic Axes -- 5.10 Scatter Matrices -- 5.11 Heatmaps -- 5.12 Correlations -- 5.13 Anscombe's Quartet and the Limits of Numbers -- 5.14 Time Series -- 5.15 Further Reading -- 5.16 Glossary -- Chapter 6 Machine Learning Overview -- 6.1 Historical Context -- 6.2 Supervised versus Unsupervised6.3 Training Data, Testing Data, and the Great Boogeyman of Overfitting -- 6.4 Further Reading -- 6.5 Glossary -- Chapter 7 Interlude: Feature Extraction Ideas -- 7.1 Standard Features -- 7.2 Features That Involve Grouping -- 7.3 Preview of More Sophisticated Features -- 7.4 Defining the Feature You Want to Predict -- Chapter 8 Machine Learning Classification -- 8.1 What Is a Classifier, and What Can You Do with It? …”
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