◼ 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 Objects and Attribute Types ◼ Basic Statistical Descriptions of Data ◼ Data Visualization ◼ Measuring Data Similarity and Dissimilarity ◼ Summary

3.1 Understand the basic definitions and concepts of data warehousing 3.2 Understand data warehousing architectures 3.3 Describe the processes used in developing and managing data warehouses 3.4 Explain data warehousing operations 3.5 Explain the role of data warehouses in decision support

 1.1 What’s big data?  1.2 Overview of data analysis  1.3 Overview of data mining  1.4 Make requirement for different professional applications

Nominal data is the simplest form of data in which data falls into unordered Types of Data to represent non-numeric categories Nominal data can have two or more categories Where there are only two categories variable(or indicator) is generally referred to as dichotomous or binary Types of data Nominal scales

 Data Integration  Data Reduction  Data Transformation Open Section

4.1 Data Types And Data Structure 4.2 Typical Data Structure – Stacks And Queues

Data Objects and Attribute Types Basic Statistical Descriptions of Data Data Visualization Measuring Data Similarity and Dissimilarity Summary

◼ Data Objects and Attribute Types ◼ Basic Statistical Descriptions of Data ◼ Data Visualization ◼ Measuring Data Similarity and Dissimilarity ◼ Summary

J Chapter 3- Learning objectives Describe data using measures of central tendency and dispersion for a set of individual data values, and for a set of grouped data Convert data to standardized values Use the computer to visually represent data