Handbook of Statistical Analysis and Data Mining Applications
2nd Edition
Published on 9. November 2017
822 pages
978-0-12-416645-5 (ISBN)
Description
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.
This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce.
- Includes input by practitioners for practitioners
- Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
- Contains practical advice from successful real-world implementations
- Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
- Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
More details
Other editions
Additional editions
Ken Yale | Robert Nisbet | Gary D. Miner
Handbook of Statistical Analysis and Data Mining Applications
Book
11/2017
2nd Edition
Academic Press
€97.79
Article exhausted; check for reprint
Previous edition
Robert Nisbet | John Elder | Gary D. Miner
Handbook of Statistical Analysis and Data Mining Applications
E-Book
05/2009
1st Edition
Academic Press
€70.95
Available for download
Persons
Dr. Kenneth Yale has a track record of Business Development, Product Innovation, and Strategy in both entrepreneurial and large companies across healthcare industry verticals, including Health Payers, Life Sciences, and Government Programs. He is an agile executive who identifies future industry trends and seizes opportunities to build sustainable businesses. His accomplishments include innovations in Health Insurance, Care Management, Data Science, Big Data Healthcare Analytics, Clinical Decision Support, and Precision Medicine.His prior experience includes: medical director and vice president of clinical solutions at ActiveHealth Management/Aetna, chief executive of innovation incubator business unit at UnitedHealth Group Community & State, strategic counsel for Johnson & Johnson, corporate vice president of CorSolutions and Matria Healthcare, senior vice president and general counsel at EduNeering, and founder and CEO of Advanced Health Solutions. Dr. Yale previously worked in the federal government as Commissioned Officer in the US Public Health Service, Legislative Counsel in the US Senate, Special Assistant to the President and Executive Director of the White House Domestic Policy Council, and Chief of Staff of the White House Office of Science and Technology.Dr. Yale provides leadership and actively participates with industry organizations, including the American Medical Informatics Association - Workgroup on Genomics and Translational Bioinformatics; Bloomberg/BNA Health Insurance Advisory Board; Healthcare Information and Management Systems Society; and the URAC Accreditation Organization. He is a frequent speaker and author on health and technology topics, including the books "Managed Care Compliance Guide, "Clinical Integration: Population Health and Accountable Care, tutorial author in "Practical Predictive Analytics and Decisioning Systems for Medicine, and editor with "Statistical Analysis and Data Mining Applications Second Edition.Bob Nisbet, PhD, is a Data Scientist, currently modeling precancerous colon polyp presence with clinical data at the UC-Irvine Medical Center. He has experience in predictive modeling in Telecommunications, Insurance, Credit, Banking. His academic experience includes teaching in Ecology and in Data Science. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank & Trust in charge of business intelligence reporting and customer relationship management (CRM) modeling.
Content
Part 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process1. The Background for Data Mining Practice2. Theoretical Considerations for Data Mining3. The Data Mining and Predictive Analytic Process4. Data Understanding and Preparation5. Feature Selection6. Accessory Tools for Doing Data Mining
Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas7. Basic Algorithms for Data Mining: A Brief Overview8. Advanced Algorithms for Data Mining9. Classification10. Numerical Prediction11. Model Evaluation and Enhancement12. Predictive Analytics for Population Health and Care13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors14. Customer Response Modeling15. Fraud Detection
Part 3: Tutorials And Case StudiesTutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta)Tutorial C Case Study-Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX)Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality DataTutorial E Feature Selection in KNIMETutorial F Medical/Business TutorialTutorial G A KNIME Exercise, Using Alzheimer's Training Data of Tutorial FTutorial H Data Prep 1-1: Merging Data SourcesTutorial I Data Prep 1-2: Data DescriptionTutorial J Data Prep 2-1: Data Cleaning and RecodingTutorial K Data Prep 2-2: Dummy Coding Category VariablesTutorial L Data Prep 2-3: Outlier HandlingTutorial M Data Prep 3-1: Filling Missing Values With ConstantsTutorial N Data Prep 3-2: Filling Missing Values With FormulasTutorial O Data Prep 3-3: Filling Missing Values With a ModelTutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text MinerTutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10Tutorial R Example With C&RT to Predict and Display Possible Structural RelationshipsTutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client
Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes16. The Apparent Paradox of Complexity in Ensemble Modeling17. The "Right Model" for the "Right Purpose": When Less Is Good Enough18. A Data Preparation Cookbook19. Deep Learning20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models"21. Ethics and Data Analytics22. IBM Watson
