Data Science for Fundraising

Discover the techniques used by the top R programmers to generate data-driven solutions.Although the non-profit industry has advanced using CRMs and donor databases, it has not fully explored the data stored in those databases. Meanwhile, the data scientists, in the for-profit industry, using sophis [...]
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Discover the techniques used by the top R programmers to generate data-driven solutions.Although the non-profit industry has advanced using CRMs and donor databases, it has not fully explored the data stored in those databases. Meanwhile, the data scientists, in the for-profit industry, using sophisticated tools, have generated data-driven results and effective solutions for several challenges in their organizations.Wouldn't you like to learn these data science techniques to solve fundraising problems?After reading Data Science for Fundraising, you can:
? Begin your data science journey with R
? Import data from Excel, text and CSV files, and databases, such as sqllite and Microsoft's SQL Server
? Apply data cleanup techniques to remove unnecessary characters and whitespace
? Manipulate data by removing, renaming, and ordering rows and columns
? Join data frames using dplyr
? Perform Exploratory Data Analysis by creating box-plots, histograms, and Q-Q plots
? Understand effective data visualization principles, best practices, and techniques
? Use the right chart type after understanding the advantages and disadvantages of different chart types
? Create beautiful maps by ZIP code, county, and state
? Overlay maps with your own data
? Create elegant data visualizations, such as heat maps, slopegraphs, and animated charts
? Become a data visualization expert
? Create Recency, Frequency, Monetary (RFM) models
? Build predictive models using machine learning techniques, such as K-nearest neighbor, Naive Bayes, decision trees, random forests, gradient boosting, and neural network
? Build deep learning neural network models using TensorFlow
? Predict next transaction amount using regression and machine learning techniques, such as neural networks and quantile regression
? Segment prospects using clustering and association rule mining
? Scrape data off the web and create beautiful reports from that data
? Predict sentiment using text mining and Twitter data
? Analyze social network data using measures, such as betweenness, centrality, and degrees
? Visualize social networks by building beautiful static and interactive maps
? Learn the industry-transforming trendsRegardless of your skill level, you can equip yourself and help your organization succeed with these data science techniques using R.