Data Analytics Made Accessible

This book fills the need for a concise and conversational book on the hot and growing field of Data Science. Easy to read and informative, this lucid and constantly updated book covers everything important, with concrete examples, and invites the reader to join this field. University of Texas calls it #1 read for Data Analysts. https://techbootcamps.utexas.edu/blog/4-books-every-data-analyst-read/ The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Finally, it includes a tutorial for R. The 2019 edition contained expanded primers on Big Data, Artificial Intelligence, and Data Science careers, and a full tutorial on Python. The 2020 edition contains a new chapter on Data Ownership and Privacy, as these issues have become increasingly important. The book has proved very popular throughout the world. Dozens of universities around the world have adopted it as a textbook for their courses. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again as a reference book for insights and techniques. Preface Chapter 1: Wholeness of Data Analytics Business Intelligence Caselet: MoneyBall - Data Mining in Sports Pattern Recognition Data Processing Chain Data Database Data Warehouse Data Mining Data Visualization Organization of the book Review Questions Section 1 Chapter 2: Business Intelligence Concepts and Applications Caselet: Khan Academy – BI in Education BI for better decisions Decision types BI Tools BI Skills BI Applications Customer Relationship Management Healthcare and Wellness Education Retail Banking Financial Services Insurance Manufacturing Telecom Public Sector Conclusion Review Questions Liberty Stores Case Exercise: Step 1 Chapter 3: Data Warehousing Caselet: University Health System – BI in Healthcare Design Considerations for DW DW Development Approaches DW Architecture Data Sources Data Loading Processes Data Warehouse Design DW Access DW Best Practices Conclusion Review Questions Liberty Stores Case Exercise: Step 2 Chapter 4: Data Mining Caselet: Target Corp – Data Mining in Retail Gathering and selecting data Data cleansing and preparation Outputs of Data Mining Evaluating Data Mining Results Data Mining Techniques Tools and Platforms for Data Mining Data Mining Best Practices Myths about data mining Data Mining Mistakes Conclusion Review Questions Liberty Stores Case Exercise: Step 3 Chapter 5: Data Visualization Caselet: Dr Hans Gosling - Visualizing Global Public Health Excellence in Visualization Types of Charts Visualization Example Visualization Example phase -2 Tips for Data Visualization Conclusion Review Questions Liberty Stores Case Exercise: Step 4 Section 2 Chapter 6: Decision Trees Caselet: Predicting Heart Attacks using Decision Trees Decision Tree problem Decision Tree Construction Lessons from constructing trees Decision Tree Algorithms Conclusion Review Questions Liberty Stores Case Exercise: Step 5 Chapter 7: Regression Caselet: Data driven Prediction Markets Correlations and Relationships Visual look at relationships Regression Exercise Non-linear regression exercise Logistic Regression Advantages and Disadvantages of Regression Models Conclusion Review Exercises: Liberty Stores Case Exercise: Step 6 Chapter 8: Artificial Neural Networks Caselet: IBM Watson - Analytics in Medicine Business Applications of ANN Design Principles of an Artificial Neural Network Representation of a Neural Network Architecting a Neural Network Developing an ANN Advantages and Disadvantages of using ANNs Conclusion Review Exercises Chapter 9: Cluster Analysis Caselet: Cluster Analysis Applications of Cluster Analysis Definition of a Cluster Representing clusters Clustering techniques Clustering Exercise K-Means Algorithm for clustering Selecting the number of clusters Advantages and Disadvantages of K-Means algorithm Conclusion Review Exercises Liberty Stores Case Exercise: Step 7 Chapter 10: Association Rule Mining Caselet: Netflix: Data Mining in Entertainment Business Applications of Association Rules Representing Association Rules Algorithms for Association Rule Apriori Algorithm Association rules exercise Creating Association Rules Conclusion Review Exercises Liberty Stores Case Exercise: Step 8 Section 3 Chapter 11: Text Mining Caselet: WhatsApp and Private Security Text Mining Applications Text Mining Process Term Document Matrix Mining the TDM Comparing Text Mining and Data Mining Text Mining Best Practices Conclusion Review Questions Chapter 12: Web Mining Web content mining Web structure mining Web usage mining Web Mining Algorithms Conclusion Review Questions Chapter 13: Big Data Caselet: Personalized Promotions at Sears Defining Big Data Big Data Landscape Business Implications of Big Data Technology Implications of Big Data Big Data Technologies Management of Big Data Conclusion Review Questions Chapter 14: Data Modeling Primer Evolution of data management systems Relational Data Model Implementing the Relational Data Model Database management systems (DBMS) Structured Query Language Conclusion Review Questions Appendix 1: Data Mining Tutorial with Weka Appendix 1: Data Mining Tutorial with R Additional Resources