Synopsis "Data Science for Fundraising: Build Data-Driven Solutions Using R"
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.
