• Data Mining – From data warehousing to data mining. – Data pre-processing and data mining life-cycle. – Association and sequence analysis; classification and clustering. – Fuzzy Logic, Neural Networks, and Genetic Algorithms. – Mining Complex Data. • OLAP mining; spatial data mining; text mining; time-series data mining; web mining; visual data mining. • Data warehousing. – Introduction; basic concepts of data warehousing; data warehouse vs. Operational DB; data warehouse and the industry. – Architecture and design; two-tier and three￾tier architecture; star schema and snowflake schema; data capturing, replication, transformation and cleansing. – Data characteristics; metadata; static and dynamic data; derived data. – Data Marts; OLAP; data mining; data warehouse administration

◼ Data Preprocessing: An Overview ◼ Data Quality ◼ Major Tasks in Data Preprocessing ◼ Data Cleaning ◼ Data Integration ◼ Data Reduction ◼ Data Transformation and Data Discretization ◼ Summary

Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary

◼ Data Preprocessing: An Overview ◼ Data Quality ◼ Major Tasks in Data Preprocessing ◼ Data Cleaning ◼ Data Integration ◼ Data Reduction ◼ Data Transformation and Data Discretization ◼ Summary

Motivation: Why data mining? What is data mining? A Multi-Dimensional View of Data Mining What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Kinds of Technologies Are Used? What Kinds of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary

◼ Data Warehouse: Basic Concepts ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ Data Warehouse Design and Usage ◼ Data Warehouse Implementation ◼ Data Generalization by Attribute-Oriented Induction ◼ Summary

7.1 Learn what Big Data is and how it is changing the world of analytics 7.2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for Big Data analytics 7.4 Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies 7.6 Become familiar with select Big Data platforms and services 7.7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics

4.1 Define data mining as an enabling technology for business analytics 4.2 Understand the objectives and benefits of data mining 4.3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4.5 Learn different methods and algorithms of data mining 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining

◼ Data Warehouse: Basic Concepts ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ Data Cube Computation: Preliminary Concepts ◼ Data Cube Computation Methods ◼ Summary

 2.1 Overview of data types  2.2 Review of Data pre-processing tools and platforms  2.3 Clean, storage and management of raw data  2.4 Collections of data analysis and data mining