Topic #1: Predictive Analytics I: Data Mining Process, Methods, and Algorithms
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Read Pearson Chapter 4 – Predictive Analytics I: Data Mining Process, Methods, and Algorithms
Topic #1: Data Mining
Research the latest Privacy Issues with Data Mining and determine whether they are substantiated.
Also, research the most common mistakes and myths evolving around data mining.
Background
The Data Mining Process and manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns and trends in data include Bayes’ theorem (circa 1700s) and regression analysis (circa 1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation capabilities.
As data sets have grown and increased in complexity forming “Big Data” farms and structured Data Warehouses, “hands-on” data analysis has increasingly been enhanced with automated data processing and aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (circa 1950s), decision trees and decision rules (circa 1960s), and support vector machines (circa 1990s).
Data Mining is the process of applying these methods with the intention of uncovering hidden patterns and trends within large data warehouses. This helps to bridge the gap from applied statistics to artificial intelligence (AI), by exploiting the way data is stored and indexed in databases, thus producing the actual learning and execution of discovery algorithms, and allowing such methods to be applied to even larger data sets.
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