Handbook of Statistical Analysis and Data Mining Applications - Semantic ScholarNisbet , G. Miner , and K. Yale Eds. Academic Press , London, 2 edition, BibSonomy The blue social bookmark and publication sharing system.
Statistics intro: Mean, median, and mode - Data and statistics - 6th grade - Khan Academy
Handbook of Statistical Analysis and Data Mining Applications
Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets. It is a multi-disciplinary skill that uses machine learning, statistics, AI and database technology. The insights derived via Data Mining can be used for marketing, fraud detection, and scientific discovery, etc. In this tutorial, you will learn- What is Data Mining? First, you need to understand business and client objectives.
Note that while every book here is provided for free, consider purchasing the hard copy if you find any particularly helpful. In many cases you will find Amazon links to the printed version, but bear in mind that these are affiliate links, and purchasing through them will help support not only the authors of these books, but also LearnDataSci. Thank you for reading, and thank you in advance for helping support this website. Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Learning and Intelligent Optimization LION is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.
Artificial Intelligence A Modern Approach, 1st Edition
With data mining , a retailer can use point-of-sale records of customer purchases to develop products and promotions to appeal to specific customer segments. Data mining holds great potential to improve health systems. It uses data and analytics to identify best practices that improve care and reduce costs. Researchers use data mining approaches like multi-dimensional databases, machine learning, soft computing, data visualization and statistics. Mining can be used to predict the volume of patients in every category. Processes are developed that make sure that the patients receive appropriate care at the right place and at the right time. Data mining can also help healthcare insurers to detect fraud and abuse.