Data Mining for Business Analytics: Concepts, Techniques and Applications in Python - PDF & ePUB Download

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Download Data Mining for Business Analytics: Concepts, Techniques and Applications in Python ebook
EPUB
  • eBook:
    Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
  • Author:
    Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel
  • Edition:
    1 edition
  • Categories:
  • Data:
    2019-11-05
  • ISBN:
    1119549841
  • ISBN-13:
    9781119549840
  • Language:
    English
  • Pages:
    608
  • Format:
    EPUB

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Description of Data Mining for Business Analytics: Concepts, Techniques and Applications in Python ebook

Download Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, pdf, epub free. Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration
Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.
This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
  • A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
  • A new section on ethical issues in data mining
  • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
  • More than a dozen case studies demonstrating applications for the data mining techniques described
  • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
  • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”
?Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R 
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Content

Part I Preliminaries
Chapter 1. Introduction
Chapter 2. Overview of the Data Mining Process

Part II Data Exploration and Dimension Reduction
Chapter 3. Data Visualization
Chapter 4. Dimension Reduction

Part III Performance Evaluation
Chapter 5. Evaluating Predictive Performance

Part IV Prediction and Classification Methods
Chapter 6. Multiple Linear Regression
Chapter 7. k-Nearest Neighbors (k-NN)
Chapter 8. The Naive Bayes Classifier
Chapter 9. Classification and Regression Trees
Chapter 10. Logistic Regression
Chapter 11. Neural Nets
Chapter 12. Discriminant Analysis
Chapter 13. Combining Methods: Ensembles and Uplift Modeling

Part V Mining Relationships Among Records
Chapter 14. Association Rules and Collaborative Filtering
Chapter 15. Cluster Analysis

Part VI Forecasting Time Series
Chapter 16. Handling Time Series
Chapter 17. Regression-Based Forecasting
Chapter 18. Smoothing Methods

PART VII Data Analytics
Chapter 19. Social Network Analytics
Chapter 20. Text Mining

PART VIII Cases
Chapter 21. Cases

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