 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

◼ 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

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

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

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