Data Science for Fundraising: Build Data-Driven Solutions Using R, Paperback/Ashutosh R. Nandeshwar

Contributor(s): Author: Ashutosh R. Nandeshwar Author: Devine Rodger D iscover 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 trends Regardless of your skill level, you can equip yourself and help your organization succeed with these data science techniques using R.