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 trends

Regardless of your skill level, you can equip yourself and help your organization succeed with these data science techniques using R.

探索頂尖 R 程式設計師使用的技巧，以生成數據驅動的解決方案。

雖然非營利行業已經使用客戶關係管理系統和捐贈者資料庫進行了一些進展，但對於這些資料庫中儲存的數據尚未充分探索。與此同時，利用複雜工具的數據科學家在營利行業中為組織的多個挑戰生成了數據驅動的結果和有效解決方案。

您想學習這些數據科學技術來解決籌款問題嗎？

閱讀《Data Science for Fundraising》後，您可以：
✔ 使用 R 開始您的數據科學之旅
✔ 從 Excel、文本和 CSV 文件以及 sqllite 和 Microsoft 的 SQL Server 等數據庫中導入數據
✔ 應用數據清理技術，刪除不必要的字符和空格
✔ 通過刪除、重命名和排序行和列來操作數據
✔ 使用 dplyr 連接數據框
✔ 通過創建箱形圖、直方圖和 Q-Q 圖進行探索性數據分析
✔ 理解有效的數據可視化原則、最佳實踐和技巧
✔ 在了解不同圖表類型的優點和缺點後，選擇適當的圖表類型
✔ 通過郵遞區號、縣和州創建美麗的地圖
✔ 將地圖與您自己的數據疊加
✔ 創建優雅的數據可視化，如熱力圖、斜線圖和動態圖表
✔ 成為數據可視化專家
✔ 創建最近性、頻率、金額（RFM）模型
✔ 使用機器學習技術（如 K 最近鄰、朴素貝葉斯、決策樹、隨機森林、梯度提升和神經網絡）構建預測模型
✔ 使用 TensorFlow 構建深度學習神經網絡模型
✔ 使用回歸和機器學習技術（如神經網絡和分位數回歸）預測下一筆交易金額
✔ 使用聚類和關聯規則探勘對象進行分割
✔ 從網絡上抓取數據並從該數據創建美麗的報告
✔ 使用文本探勘和 Twitter 數據預測情感
✔ 使用介度、中心性和度量等方法分析社交網絡數據
✔ 通過構建美麗的靜態和互動地圖來可視化社交網絡
✔ 了解行業變革趨勢

無論您的技能水平如何，您都可以使用這些 R 數據科學技術裝備自己，幫助您的組織取得成功。