EZ Pass 2026 ADsP Data Analysis Associate Professional - Keybooks Data Qualification Series 9

Authors Jeon Yong-moon, Park Hyun-min · Publisher WikiBooks · Published 2026-01-02 · ISBN 9791158396510

About the Book

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Table of Contents

[Subject I] Understanding Data

Chapter 01: Understanding Data
01 Data and Information
__1. Definition of Data
__2. Data and Information
__Key Questions
02 Definition and Characteristics of Databases
__1. Database Definition
__2. Database Characteristics
__Key Questions
03 Database Utilization
__1. Database Utilization
__2. Types of Databases
__Key Questions

Chapter 02: The Value and Future of Data
01 Understanding Big Data
__1. Understanding Big Data
__2. Background of Big Data Emergence
__Key Questions
02 Value and Impact of Data
__1. Value of Big Data
__2. Impact of Big Data
__Key Questions
03 Business Models
__1. Big Data Application Cases
__2. 7 Basic Techniques for Big Data Utilization
__Key Questions
04 Risk Factors and Control Measures
__1. Risk Factors
__2. Control Measures
__Key Questions
05 Future of Big Data
__1. Three Elements of Big Data Utilization: Data, Technology, Personnel
__Key Questions

Chapter 03: Data Science and Strategic Insights for Value Creation
01 Big Data Analysis and Strategic Insights
__1. Big Data Craze and Skepticism
__2. The Core of Big Data Analysis is 'Insight', not 'Big'
__3. One-Dimensional Analysis vs. Value-Based Analysis for Strategy Derivation
__Key Questions
02 Necessary Competencies for Deriving Strategic Insights
__1. Understanding and Role of Data Science
__2. Data Science and Data Scientists
__3. Data Science: The Crossroads of Science and Humanities
__Key Questions
03 Big Data and the Future of Data Science
__1. Changes in Value Paradigm
__2. Limitations of Data Science and Humanities
__Key Questions
Past Exam Questions
Expected Questions

[Subject II] Data Analysis Planning

Chapter 01: Understanding Data Analysis Planning
01 Deriving Analysis Planning Directions
__1. Definition and Characteristics of Analysis Planning
__2. Considerations for Analysis Planning
__Key Questions
02 Analysis Methodologies
__1. Overview of Analysis Methodologies
__2. Models Based on Work Characteristics Where Analysis Methodologies Are Applied
__3. Two Traditional Analysis Methodologies
__4. Big Data Analysis Methodologies
__Key Questions
03 Identifying Analysis Tasks
__1. Overview of Identifying Analysis Tasks
__2. Top-Down Approach
__3. Bottom-Up Approach
__Key Questions
04 Analysis Project Management Plan
__1. Overview of Analysis Project Management
__2. Analysis Task Management Plan
__Key Questions

Chapter 02: Analysis Master Plan
01 Establishing a Master Plan
__1. Establishing a Master Plan
__2. Deriving and Prioritizing Implementation Tasks
__3. Establishing an Implementation Plan
__Key Questions
02 Establishing an Analysis Governance System
__1. Overview of Governance System
__2. Data Analysis Maturity Model and Level Diagnosis
__3. Establishing an Analysis Support Infrastructure Plan
__4. Establishing a Data Governance System
__5. Establishing a Data Organization and Personnel Plan
__6. Establishing an Analysis Task Management Process
__7. Analysis Training and Change Management
__Key Questions
Past Exam Questions
Expected Questions

[Subject III] Data Analysis

Chapter 01: R Basics and Data Mart
01 R Basics
__1. R Installation and Basic Usage
__2. R Basic Syntax
__Key Questions
02 Data Mart
__1. Understanding Data Mart
__2. Utilizing R Packages for Data Mart Development
__Key Questions
03 Handling Missing Values and Detecting Outliers
__1. Exploratory Data Analysis (EDA)
__2. Missing Values
__3. Outliers
__Key Questions

Chapter 02: Statistical Analysis
01 Introduction to Statistics
__1. Overview of Statistics
__2. Probability and Probability Distributions
__3. Estimation and Hypothesis Testing
__Key Questions
02 Basic Statistical Analysis
__1. t-test
__2. Analysis of Variance (ANOVA)
__3. Cross-Tabulation Analysis
__4. Correlation Analysis
__Key Questions
03 Regression Analysis
__1. Overview of Regression Analysis
__2. Simple Linear Regression Analysis
__3. Multiple Linear Regression Analysis
__4. Optimal Regression Equation
__5. Advanced Regression Analysis
__Key Questions
04 Multivariate Analysis
__1. Multidimensional Scaling
__2. Principal Component Analysis (PCA)
__Key Questions
05 Time Series Analysis
__1. Overview of Time Series Analysis
__2. Time Series Models
__Key Questions

Chapter 03: Structured Data Mining
01 Overview of Data Mining
__1. Overview of Data Mining
__2. Data Partitioning
__Key Questions
02 Classification Analysis
__1. Logistic Regression Analysis
__2. Decision Tree
__3. Ensemble Analysis
__4. Artificial Neural Network Analysis
__5. Naive Bayes Classification
__6. k-NN Algorithm
__7. Support Vector Machine
__8. Performance Evaluation of Classification Models
__Key Questions
03 Clustering Analysis
__1. Clustering Analysis
__2. Hierarchical Clustering Analysis
__3. Non-Hierarchical Clustering Analysis
__Key Questions
04 Association Analysis
__1. Overview and Measures of Association Analysis
__2. Algorithms and Characteristics of Association Analysis
__Key Questions
Past Exam Questions
Expected Questions

Practice Exam 1
Practice Exam 2
Practice Exam 3

Past Exam (45th)
Past Exam (46th)
Past Exam (47th)