Data Analytics Professional Guide (ADP)(ADsP)
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Description
Book Introduction
Today, data utilization through data processing and analysis is rapidly emerging as a key driver of national economic value creation, including increased productivity, high added value, and job creation.
In particular, data analysis, which serves as the foundation for scientific decision-making, is attracting attention as an innovative tool that contributes to improving the productivity of companies and countries.
Companies can realize increased profits by incorporating data analytics into their business strategies, and introducing data analytics into the public sector is expected to generate significant social and economic benefits.
To realize the potential of data analysis, it is essential to secure data analysis experts with the ability to present strategic directions for an organization through multifaceted data analysis.
This guide is a guide for training data analysis experts. Based on the job analysis results derived by industry, industry, and academia experts and the experience of practical experts, it presents a broad and detailed overview of the knowledge and skills required of data analysis experts, from data understanding, processing techniques, and analysis planning to analysis and visualization.
※The National Certified Data Analysis Professional (ADP) and Associate Professional (ADsP) qualification examinations implemented after January 1, 2017 will be based on this revised edition.
In particular, data analysis, which serves as the foundation for scientific decision-making, is attracting attention as an innovative tool that contributes to improving the productivity of companies and countries.
Companies can realize increased profits by incorporating data analytics into their business strategies, and introducing data analytics into the public sector is expected to generate significant social and economic benefits.
To realize the potential of data analysis, it is essential to secure data analysis experts with the ability to present strategic directions for an organization through multifaceted data analysis.
This guide is a guide for training data analysis experts. Based on the job analysis results derived by industry, industry, and academia experts and the experience of practical experts, it presents a broad and detailed overview of the knowledge and skills required of data analysis experts, from data understanding, processing techniques, and analysis planning to analysis and visualization.
※The National Certified Data Analysis Professional (ADP) and Associate Professional (ADsP) qualification examinations implemented after January 1, 2017 will be based on this revised edition.
index
Guide to Data Analysis Specialist/Associate Specialist Qualification Examinations
What is a data analysis expert?
The Need for Data Analysis Professional Certification
Data Analysis Specialist Job
Qualification Test Subject Guide
Qualification Test Application Guide
■ Subject Ⅰ.
Understanding data
Chapter 1.
Understanding data
Section 1 Data and Information
Section 2 Definition and Characteristics of Databases
Section 3 Database Utilization
Chapter Summary
Practice problems
Chapter 2.
The Value and Future of Data
Section 1 Understanding Big Data
Section 2: The Value and Impact of Big Data
Section 3 Business Model
Section 4 Risk Factors and Control Measures
Section 5 Big Data of the Future
Chapter Summary
Practice problems
Chapter 3.
Data Science and Strategic Insights for Value Creation
Section 1 Big Data Analysis and Strategic Insights
Section 2: Required Capabilities for Deriving Strategic Insights
Section 3: Big Data and the Future of Data Science
Chapter Summary
Practice problems
■ Subject II.
Understanding data processing technologies
Chapter 1.
Data processing process
Section 1 ETL (Extraction, Transformation and Load)
Section 2 CDC (Change Data Capture)
Section 3 EAI (Enterprise Application Integration)
Section 4 Summary of Data Linkage and Integration Techniques
Section 5: Processing large amounts of unstructured data
Chapter Summary
Practice problems
Chapter 2.
Big data processing technology
Section 1 Distributed Data Storage Technology
Section 2 Distributed Computing Technology
Section 3 Cloud Infrastructure Technology
Chapter Summary
Practice problems
■ Subject III.
Data Analysis Planning
Chapter 1.
Understanding Data Analysis Planning
Section 1: Deriving Analysis Planning Direction
Section 2 Analysis Methodology
Section 3: Identifying Analysis Tasks
Section 4 Analysis Project Management Plan
Chapter Summary
Practice problems
Chapter 2.
Analysis Master Plan
Section 1 Establishing a Master Plan
Section 2 Establishing an Analysis Governance System
Chapter Summary
Practice problems
■ Subject IV.
data analysis
Chapter 1.
R Basics and Data Marts
Section 1 R Basics
Section 2 Data Mart
Section 3 Handling Missing Values and Detecting Outliers
Chapter Summary
Practice problems
Chapter 2.
Statistical analysis
Section 1 Introduction to Statistics
Section 2 Basic Statistical Analysis
Section 3 Multivariate Analysis
Section 4 Time Series Forecasting
Section 5 Principal Component Analysis
Chapter Summary
Practice problems
Chapter 3.
Structured Data Mining
Section 1: Overview of Data Mining
Section 2 Classification Analysis
Section 3 Cluster Analysis
Section 4 Association Analysis
Chapter Summary
Practice problems
Chapter 4.
Unstructured data mining
Section 1 Text Mining
Section 2 Social Network Analysis
Chapter Summary
Practice problems
■ Subject V.
Data visualization
Chapter 1.
Visualization Insight Process
Section 1: The Meaning of the Visualization Insight Process
Section 2 Exploration (Stage 1)
Section 3 Analysis (Step 2)
Section 4 Utilization (Step 3)
Chapter Summary
Practice problems
Chapter 2.
Visualization Design
Section 1 Definition of Visualization
Section 2 Visualization Process
Section 3 Visualization Methods
Section 4 Big Data and Visualization Design
Chapter Summary
Practice problems
Chapter 3.
Visualization implementation
Section 1 Overview of Visualization Implementation
Section 2: Implementing Visualization Using Analysis Tools: R
Section 3 Library-based Visualization Implementation: Djs
Chapter Summary
Practice problems
Appendix A.
Excerpts from color illustrations (5 subjects)
Appendix B.
Practice Problem Answers and Explanations
Appendix C.
Search
References
What is a data analysis expert?
The Need for Data Analysis Professional Certification
Data Analysis Specialist Job
Qualification Test Subject Guide
Qualification Test Application Guide
■ Subject Ⅰ.
Understanding data
Chapter 1.
Understanding data
Section 1 Data and Information
Section 2 Definition and Characteristics of Databases
Section 3 Database Utilization
Chapter Summary
Practice problems
Chapter 2.
The Value and Future of Data
Section 1 Understanding Big Data
Section 2: The Value and Impact of Big Data
Section 3 Business Model
Section 4 Risk Factors and Control Measures
Section 5 Big Data of the Future
Chapter Summary
Practice problems
Chapter 3.
Data Science and Strategic Insights for Value Creation
Section 1 Big Data Analysis and Strategic Insights
Section 2: Required Capabilities for Deriving Strategic Insights
Section 3: Big Data and the Future of Data Science
Chapter Summary
Practice problems
■ Subject II.
Understanding data processing technologies
Chapter 1.
Data processing process
Section 1 ETL (Extraction, Transformation and Load)
Section 2 CDC (Change Data Capture)
Section 3 EAI (Enterprise Application Integration)
Section 4 Summary of Data Linkage and Integration Techniques
Section 5: Processing large amounts of unstructured data
Chapter Summary
Practice problems
Chapter 2.
Big data processing technology
Section 1 Distributed Data Storage Technology
Section 2 Distributed Computing Technology
Section 3 Cloud Infrastructure Technology
Chapter Summary
Practice problems
■ Subject III.
Data Analysis Planning
Chapter 1.
Understanding Data Analysis Planning
Section 1: Deriving Analysis Planning Direction
Section 2 Analysis Methodology
Section 3: Identifying Analysis Tasks
Section 4 Analysis Project Management Plan
Chapter Summary
Practice problems
Chapter 2.
Analysis Master Plan
Section 1 Establishing a Master Plan
Section 2 Establishing an Analysis Governance System
Chapter Summary
Practice problems
■ Subject IV.
data analysis
Chapter 1.
R Basics and Data Marts
Section 1 R Basics
Section 2 Data Mart
Section 3 Handling Missing Values and Detecting Outliers
Chapter Summary
Practice problems
Chapter 2.
Statistical analysis
Section 1 Introduction to Statistics
Section 2 Basic Statistical Analysis
Section 3 Multivariate Analysis
Section 4 Time Series Forecasting
Section 5 Principal Component Analysis
Chapter Summary
Practice problems
Chapter 3.
Structured Data Mining
Section 1: Overview of Data Mining
Section 2 Classification Analysis
Section 3 Cluster Analysis
Section 4 Association Analysis
Chapter Summary
Practice problems
Chapter 4.
Unstructured data mining
Section 1 Text Mining
Section 2 Social Network Analysis
Chapter Summary
Practice problems
■ Subject V.
Data visualization
Chapter 1.
Visualization Insight Process
Section 1: The Meaning of the Visualization Insight Process
Section 2 Exploration (Stage 1)
Section 3 Analysis (Step 2)
Section 4 Utilization (Step 3)
Chapter Summary
Practice problems
Chapter 2.
Visualization Design
Section 1 Definition of Visualization
Section 2 Visualization Process
Section 3 Visualization Methods
Section 4 Big Data and Visualization Design
Chapter Summary
Practice problems
Chapter 3.
Visualization implementation
Section 1 Overview of Visualization Implementation
Section 2: Implementing Visualization Using Analysis Tools: R
Section 3 Library-based Visualization Implementation: Djs
Chapter Summary
Practice problems
Appendix A.
Excerpts from color illustrations (5 subjects)
Appendix B.
Practice Problem Answers and Explanations
Appendix C.
Search
References
GOODS SPECIFICS
- Publication date: March 15, 2021
- Format: Hardcover book binding method guide
- Page count, weight, size: 988 pages | 2,094g | 195*247*54mm
- ISBN13: 9788988474839
- ISBN10: 898847483X
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