Data Mining for Business Analytics Book Review

Chapter Titles or Main Sections of the Book

1. Introduction to Data Mining and Business Analytics: Presents the basics of data mining and its role in business analytics. Discusses the various forms of data and its uses in a business context.
2. Overview of the Data Mining Process: Delves into the process of data mining, covering steps from business understanding to model deployment.
3. Data Visualization: Explores how visualizing data can aid in understanding data structure and relationships.
4. Data Preprocessing: Discusses the importance of data cleaning, transformation, and reduction in preparing data for mining.
5. Classification: Introduces popular techniques in predictive modeling including Decision Trees, Naïve Bayes, and more.
6. Prediction and Regression: Covers regression techniques including linear, logistic, and polynomial regression.
7. Association Rule Mining: Discusses techniques for discovering interesting relationships in large datasets.
8. Clustering: Presents clustering techniques like K-Means, hierarchical clustering, and density-based clustering.
9. Text Mining: Introduces the basics of text mining, its techniques and application in business scenarios.
10. Advanced Topics: Discusses ensemble methods, time-series analysis, and social network analysis.

Key Takeaways or Conclusions

The key takeaways from the book are that data mining is a powerful tool for business decision making and prediction. The understanding and application of appropriate data mining techniques is crucial for gaining insights from data. Lastly, R is a versatile and effective tool for implementing these techniques.

Author’s Background and Qualifications

The book is authored by Galit Shmueli, Peter C. Bruce, and Inbal Yahav, all of whom are experts in the field of data science. Shmueli is a distinguished professor of data analytics, Bruce is an experienced data scientist and founder of a renowned data science firm, and Yahav is a professor with extensive research in data analytics.

Comparison to Other Books on the Same Subject

Compared to other books on the subject, “Data Mining for Business Analytics” stands out because of its emphasis on practical, hands-on application of data mining techniques using R. This is unlike other books that may only provide a theoretical understanding or use other software packages.

Target Audience or Intended Readership

The book is intended for business analysts, data scientists, and students in business analytics or data science programs. It requires some background in statistics and an interest in R programming.

Reception or Critical Response to the Book

The book has been widely appreciated for its clear, systematic approach to the subject. It has been praised for its hands-on approach and inclusion of real-world business examples.

Publisher and First Published Date

The book was first published on April 27, 2017, by Wiley.

Recommendations (Other Similar Books on the Same Topic)

• (Amazon) “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett.
• (Amazon) “Applied Predictive Modeling” by Max Kuhn and Kjell Johnson.
• (Amazon) “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

Where to Buy

Final Thoughts

The book’s biggest takeaway is: Data mining, when applied correctly using powerful tools like R, can significantly enhance business decision making and predictive capabilities.