Return HomeCERTIFIED DATA ANALYST LEVEL I EXAMINATION OUTLINE
一、Overall Objectives
CDA (Certified Data Analyst), or "CDA Data Analyst", is a professional and authoritative international qualification certification for entire industry under the background of digital economy and the trend of artificial intelligence era. It aims to improve the public digital skills, help digital transformation of enterprises, and promote digital development of industry. [The Certified Data Analyst (CDA) Talent Industry Standard] is a scientific, professional, international talent skill guideline targeting data-related positions. CDA Exam Outline defines the examination range and key points. Candidates can refer to the outline for acquiring the needed skills and knowledge to become a professional data analyst when preparing the exam.
二、Exam Format and Structure
Exam method: Offline written exam and computer-based exams
Exam question type: Objective choice questions （80 single choice questions, 20 multiple choice questions, 20 content related questions, 20 case analysis questions）
Exam duration: 120 minutes
Exam scores: The final exam scores are classified into four grades: A, B, C, and D. Passing grades include A, B, and C, while D is the failing grade.
Exam requirements: closed book computer-based exams, without carrying calculator and other exam related supplies.
三、Knowledge Requirements
The three different mastery levels that candidates need to obtain for different types of data analysis knowledge are comprehension, competency, and application. Exam candidates should proceed with their studies based on these different knowledge requirements.
1. Comprehension: Candidates should understand and grasp key points in data analysis regulations, understand the connotation and extension of these key points, distinguish the differences and relation of these key points, and correctly elaborate each key point.
2. Competency: Candidates should master important data analysis knowledge, understand, and memorize relevant theories and methods. They must be able to logically explain data analysis knowledge based on different requirements. Candidates’ knowledgeability and competency with different types of data analysis is the key of this exam.
3. Application: Candidates should be able to demonstrate their ability to apply data analysis theory in practice while combining related tools for commercial application, and propose specific implementation procedures and the strategy to problems based on specific requirements or conditions.
四、Exam subjects
PART 1 Overview of Data Analysis and Professional Ethics (30%)
a. Data analysis concepts, methodology, and roles (1%)
b. Professional ethics and code of conduct for data analysts（1%）
c. Big data legislation, security and privacy（1%）
Part 2 Data Structure (15%)
a. Table structure data characteristics (2%)
b. Table structure data acquisition, reference, query and calculation (3%)
c. List structure data characteristics (5%)
d. List structure data acquisition, processing and use (5%)
PART 3 Database Application (17%)
a. Database related concepts (1%)
b. DDL data definition language (2%)
c. DML data manipulation language (2%)
d. Single table query (3%)
e. Multi-table query (3%)
f. Subquery (3%)
g. Database functions (3%)
Part 4 Descriptive Statistical Analysis (10%)
a. Basic concepts of Statistics (2%)
b. Descriptive statistics of data (3%)
c. Statistical distribution (3%)
d. Correlation analysis (2%)
Part 5 Multidimensional Data Perspective Analysis (10%)
a. Multi table perspective analysis logic (3%)
b. Multidimensional data model (3%)
c. Perspective analysis method (4%)
Part 6 Business Data Analysis (30%)
a. Data driven business management method (3%)
b. Application and design of index (12%)
c. Business analysis method (15%)
Customer analysis
Commodity analysis
Flow and transformation analysis
Behavior effect analysis
Business analysis model
Business analysis method
Part 7 business analysis report and data visualization report (15%)
a. Visual analysis chart (5%)
b. Write business analysis report (5%)
c. Create data visualization report (5%)
五、Subject contents
1、Data analysis concepts, methodology, and roles
[Comprehension]
Understand basic concepts of data analysis (data analysis, data mining, big data)
Understand data analysis purpose and significance
Understand data analysis method and process
Understand different roles and responsibilities of data analysis
Understand data analysis purpose and significance
Understand data analysis method and process
Understand different roles and responsibilities of data analysis
2、Professional ethics and code of conduct for data analysts
[Comprehension]
Understand data analyst professional ethics
Understand professional code of conduct for data analysts
Understand professional code of conduct for data analysts
3、Big data legislation, security and privacy
[Comprehension]
Understand legal requirements related to foreign privacy (refer to "Guide to International Data Protection Rules")
Understand the history and outlook of domestic big data legislation (refer to " Report on China’s development of big data under the rule of law")
Understand data usage rights of companies and individuals in the EU’s General Data Protection Regulation (GDPR)
Understand the history and outlook of domestic big data legislation (refer to " Report on China’s development of big data under the rule of law")
Understand data usage rights of companies and individuals in the EU’s General Data Protection Regulation (GDPR)
General requirements
Candidates should understand data characteristics of table structure and list structure, understand list structure and table structure data acquisition operation method, understand the logic of list structure data connection and summary, be able to apply list structure connection and summary logic to associate multiple tables for summary evaluation calculation, and be able to make ER diagram.
1、Table structure data characteristics
[Comprehension]
Understand table structure data concept
Understand table structure data processing tool
Understand table structure data processing tool
[Competency]
Familiar with table structure data features
2、Table structure data acquisition, reference, query and calculation
[Comprehension]
Understand table structure data acquisition method
[Competency]
Familiar with characteristics of cell range
[Application]
Capable of applying reference method of table structure data
Capable of applying query method of table structured data
Capable of applying common functions of table structured data
Capable of applying query method of table structured data
Capable of applying common functions of table structured data
3、List structure data characteristics
[Competency]
Understand the meaning of primary keys
Understand the meaning of dimensions and measures
Understand missing values
Familiar with list structure data characteristics
Familiar with the difference between list structure data and table structure data
Understand the meaning of dimensions and measures
Understand missing values
Familiar with list structure data characteristics
Familiar with the difference between list structure data and table structure data
4、List structure data acquisition, processing and use
[Comprehension]
Understand list structure data acquisition channels and methods
[Competency]
Familiar with list structure data connection logic
Familiar with list structure data summary logic
Familiar with list structure data summary logic
Familiar with list structure data summary logic
Familiar with list structure data summary logic
[Application]
Capable of applying E-R diagram
Capable of calculating the connection summary value of two tables
Capable of calculating the connection summary value of two tables
General requirements
Candidates should understand the basic concepts of databases, understand DDL and DML languages, be able to use query language to obtain accurate and complete data information from the database according to business needs and data characteristics, and be able to apply database functions for data processing and calculations.
1、Database related concepts
[Comprehension]
Understand database classification
Understand SQL functions
Understand SQL functions
[Competency]
Familiar with the relationship among database, database management system and SQL
2、DDL data definition language
[Comprehension]
Understand the basic structure of database
[Competency]
Familiar with data types
Familiar with constraint condition
Familiar with constraint condition
[Application]
Capable of creating, selecting and deleting database
Capable of creating, modifying and deleting tables
Capable of creating, modifying and deleting tables
3、DML data manipulation language
[Comprehension]
Understand steps to add data
[Competency]
Familiar with syntax rules for adding, modifying and deleting data
[Application]
Adding data
Modifying data
Deleting data
Modifying data
Deleting data
4、Single table query
[Comprehension]
Virtual result set
[Competency]
Operator
Understand writing order and execution logic of SQL statements
Understand writing order and execution logic of SQL statements
[Application]
Basic query: de-duplicate query, set alias
Condition query: multi-condition query, null query, fuzzy query
Group query: group aggregation, filter after group
Sort query results and limit the number of query results
Condition query: multi-condition query, null query, fuzzy query
Group query: group aggregation, filter after group
Sort query results and limit the number of query results
5、Multi-table query
[Comprehension]
Correspondence: one-to-one, one-to-many, many-to-many
Connection mode: internal connection, left connection and right connection
Connection conditions: equivalent connection, unequal value connection
Connection mode: internal connection, left connection and right connection
Connection conditions: equivalent connection, unequal value connection
[Competency]
Familiar with connect query logic and joint query rules
[Application]
Connection query: inner connection, left connection and right connection
Joint query: de-duplication, not de-duplication
Joint query: de-duplication, not de-duplication
6、Subquery
[Comprehension]
Subquery classification
[Competency]
Subquery position, subquery operator
[Application]
Subquery syntax rules
Subquery optimization
Subquery optimization
7、Database functions
[Comprehension]
Calculated field
[Competency]
Functions and parameters
[Application]
Capable of applying mathematical functions, string functions, date and time functions, grouping and merging functions, logical functions
General requirements
Candidates should understand the basic concepts of statistics, the knowledge content of descriptive statistics, understand the definition and application scenarios of descriptive statistics charts, be able to apply descriptive statistics knowledge description and explore business problems.
1、Basic concepts of Statistics
[Competency]
Familiar with the meaning and application of Statistics
Familiar with basic concepts of statistics: data, population, sample, parameter and variable
Familiar with basic concepts of statistics: data, population, sample, parameter and variable
2、Descriptive statistics of data
[Comprehension]
Descriptive statistical charts: histogram, scatter plot, box plot
Description of concentration trend: mode, median, quantile and average
Description of the degree of dispersion: range, variance, standard deviation, coefficient of dispersion, coefficient of variation
Description of distribution morphology: skewness and kurtosis
Description of concentration trend: mode, median, quantile and average
Description of the degree of dispersion: range, variance, standard deviation, coefficient of dispersion, coefficient of variation
Description of distribution morphology: skewness and kurtosis
[Application]
Capable of applying descriptive statistical knowledge to describe business data with appropriate data characteristics, explaining business problems based on data description characteristics, exploring the causes of problems, and proposing solutions to problems
3、Statistical distribution
[Competency]
Two-point distribution, binomial distribution, normal distribution, X2 distribution,T distribution, F distribution
4、Correlation analysis
[Competency]
Description of correlation analysis: scatter plot, types of correlation analysis
Measurement of correlation: correlation coefficient
Measurement of correlation: correlation coefficient
General requirements
Candidates should understand the value of multidimensional data model, understand the logic of multidimensional data model, understand the principle of perspective analysis, and be able to use multidimensional data model combined with appropriate perspective methods to observe business problems and achieve business insight.
1、Part 5 Multidimensional Data Perspective Analysis
[Competency]
Familiar with the value of perspective analysis
Understand the connection and perspective logic in a multi-table environment
Understand the connection and perspective logic in a multi-table environment
[Application]
Capable of understanding the business dimension and business meaning represented by the table through the fields of the table, and using the business meaning of the table to push back the primary key, dimension, and measurement attributes of the fields in the table
2、Multidimensional Data Perspective Analysis
[Comprehension]
Understand the business implications of using a multidimensional data model
[Competency]
Familiar with the creation method of multidimensional data model
Familiar with the relationship between the connection method and the summary result in the multidimensional data model
Familiar with the differences between summary dimensions and filtering dimensions under the multidimensional data model and their respective applicable scenarios
Familiar with the relationship between the connection method and the summary result in the multidimensional data model
Familiar with the differences between summary dimensions and filtering dimensions under the multidimensional data model and their respective applicable scenarios
[Application]
Capable of sorting out business clues through the 5W2H thinking model and collect complete multi-table data.
Capable of creating a complete, accurate and comprehensive multi-dimensional data model according to business requirements and correct connection relationships
Capable of deriving the scope of business problems that can be explored based on the multi-dimensional data model to achieve business insight
Capable of creating a complete, accurate and comprehensive multi-dimensional data model according to business requirements and correct connection relationships
Capable of deriving the scope of business problems that can be explored based on the multi-dimensional data model to achieve business insight
3、Perspective analysis method
[Comprehension]
Understand the value and significance of perspective analysis
[Competency]
Familiar with basic perspective rules: sum, average, count, maximum and minimum
Familiar with conditional filtering perspective rules: multi-condition perspective calculation, perspective calculation of different levels of dimensions
Familiar with basic comparison calculation rules: average ratio, benchmark ratio, standard ratio, percentage, difference percentage
Familiar with the perspective calculation rules under the time dimension: perspective calculation rules under different time periods and different time displacements
Familiar with the difference between row perspective and field perspective
Familiar with conditional filtering perspective rules: multi-condition perspective calculation, perspective calculation of different levels of dimensions
Familiar with basic comparison calculation rules: average ratio, benchmark ratio, standard ratio, percentage, difference percentage
Familiar with the perspective calculation rules under the time dimension: perspective calculation rules under different time periods and different time displacements
Familiar with the difference between row perspective and field perspective
[Application]
Be able to select and create correct perspective rules according to business requirements
Be able to apply perspective rules to describe business problems in a correct multidimensional model
Be able to understand business problems through perspective results
When the perspective result is not consistent with the expected result, candidates should be able to check and track the cause of the problem
Be able to apply perspective rules to describe business problems in a correct multidimensional model
Be able to understand business problems through perspective results
When the perspective result is not consistent with the expected result, candidates should be able to check and track the cause of the problem
General requirements
Candidates should understand business data analysis methods, master business data analysis processes, be able to use and design to create business indicators, be able to correctly understand business problems in combination with business models and business analysis methods, find the cause of the problem, and be able to propose solutions.
1、Data driven business management method
[Competency]
Familiar with the whole process of data coming from business to business
Familiar with the value meaning of data-driven business management
Familiar with data-driven business management processes
Familiar with data-driven business management thinking
Familiar with the value meaning of data-driven business management
Familiar with data-driven business management processes
Familiar with data-driven business management thinking
[Application]
Candidates should be able to find the integration point between business analysis and business management requirements through data-driven business management processes, can correctly understand the origin and production logic of data, and can correctly use data to provide valuable data analysis results for business management
2、Application and design of index
[Comprehension]
Index functions
[Competency]
Familiar with the thinking process and analysis methods starting from the index results to the landing of business behaviors
Familiar with the relationship between index and perspective calculations
Familiar with common index:
Traffic related index
Conversion related index
Operation and sales related index
Inventory index
Common financial index
Performance index
Customer-related index
Familiar with the design index method of dismantling business requirements
Familiar with the relationship between index and perspective calculations
Familiar with common index:
Traffic related index
Conversion related index
Operation and sales related index
Inventory index
Common financial index
Performance index
Customer-related index
Familiar with the design index method of dismantling business requirements
[Application]
Able to gain insights into business issues and impacts based on index results
Able to select appropriate index for observation according to business scenarios
Able to design new index according to business needs and improve the indicator system
Able to select appropriate index for observation according to business scenarios
Able to design new index according to business needs and improve the indicator system
3、Business analysis method
[Comprehension]
Functions of different business analysis methods
[Competency]
Familiar with the following business analysis methods:
Customer analysis: customer source analysis, customer value analysis, customer life cycle analysis, customer behavior analysis
Commodity analysis: commodity purchase, sales and inventory analysis, commodity channel analysis, commodity loss analysis, commodity price analysis
Traffic and conversion analysis: traffic conversion analysis, traffic channel analysis
Behavioral effect analysis: activity effect analysis, sales analysis, other behavioral effect analysis
Business analysis models: funnel model, RFM model, customer value model
Business analysis methods: tree structure analysis method, two-eight analysis method, four-quadrant analysis method, cohort analysis method
Customer analysis: customer source analysis, customer value analysis, customer life cycle analysis, customer behavior analysis
Commodity analysis: commodity purchase, sales and inventory analysis, commodity channel analysis, commodity loss analysis, commodity price analysis
Traffic and conversion analysis: traffic conversion analysis, traffic channel analysis
Behavioral effect analysis: activity effect analysis, sales analysis, other behavioral effect analysis
Business analysis models: funnel model, RFM model, customer value model
Business analysis methods: tree structure analysis method, two-eight analysis method, four-quadrant analysis method, cohort analysis method
[Application]
Able to apply appropriate analytical methods to solve business problems
Able to integrate data processing and analysis skills into business analysis methods to provide correct, comprehensive and objective data basis for data-driven business management
Able to integrate data processing and analysis skills into business analysis methods to provide correct, comprehensive and objective data basis for data-driven business management
General requirements
Candidates should understand how to make business analysis reports and data visualization reports, be able to write correct business analysis reports based on business requirements, and create comprehensive data visualization reports based on business requirements.
1、Visual analysis chart
[Comprehension]
Understand the difference between business charts and statistical charts
[Competency]
Familiar with business chart decision tree
Familiar with the use of comparative charts
Familiar with the use of description class charts
Familiar with the use of structural charts
Familiar with the use of sequence charts
Familiar with the use of comparative charts
Familiar with the use of description class charts
Familiar with the use of structural charts
Familiar with the use of sequence charts
[Application]
Able to choose the correct business chart to use according to data characteristics and business needs
Able to understand business issues through diagrams
Able to understand business issues through diagrams
2、Write business analysis report
[Comprehension]
Business analysis report function
[Competency]
Familiar with the business analysis report writing process
Familiar with the precautions for writing business analysis reports
Familiar with business analysis report design method
Familiar with the precautions for writing business analysis reports
Familiar with business analysis report design method
[Application]
Able to choose the correct report argument based on business needs
Able to collect and display sufficient and correct data basis based on the report's arguments
Able to write reasonable and rigorous analysis reports and put forward valuable analysis suggestions
Able to collect and display sufficient and correct data basis based on the report's arguments
Able to write reasonable and rigorous analysis reports and put forward valuable analysis suggestions
3、Create data visualization report
[Comprehension]
The function of data visualization report
[Competency]
Familiar with the differences between data visualization reports and business analysis reports
Familiar with the creation process of data visualization reports
Familiar with the design ideas of data visualization reports
Familiar with the application method of data visualization report
Familiar with the creation process of data visualization reports
Familiar with the design ideas of data visualization reports
Familiar with the application method of data visualization report
[Application]
Able to design data visualization report content that can be implemented according to business needs
Able to transform abstract business requirements into concrete data dimensions and measurement descriptions
Able to produce data visualization reports that can clearly, accurately and comprehensively describe business problems and show comprehensive business scenarios
Able to transform abstract business requirements into concrete data dimensions and measurement descriptions
Able to produce data visualization reports that can clearly, accurately and comprehensively describe business problems and show comprehensive business scenarios
六、Recommended Reading
Notes: Candidates can select reading material from the list of recommended books based on their needs. Candidates do not have to read all recommended books but can study on the key points highlighted in the exam outline.
[1] Wang Yingying. MySQL 8 From entry to proficiency [M]. Tsinghua University Press, 2019. (Optional)
[2] MICK.SQL Basic Course [M]. Advanced SQL Course [M]. Posts and Telecom Press 2017. (Optional)
[3] Huang Jinhua. Getting started with MySQL is very simple [M]. Tsinghua University Press, 2011. (Optional)
[4] Stephenson, Jin Lao, Jones. SQL Introduction Classic (5th Edition) [M]. Posts and Telecom Press, 2011. (Optional)
[5] Jia Junping, He Xiaoqun, Jin Yongjin. Statistics (7th edition) [M]. China Renmin University Press, 2018. (Optional)
[6] Huang Chengming. Data Management (1st Edition) [M]. Electronic Industry Press, 2014. (Optional)
[7] Jingdong Data Innovation Group. Jingdong Platform Data Operation (1st Edition) [M]. Electronic Industry Press, 2016. (Optional)
[8] Liu Baohong, Zhao Ling, etc. The three lines of defense in the supply chain (1st Edition) [M]. Mechanical Industry Press 2020. (Optional)
[9] Chen Zhe. Utilizing data: data analysis to drive business (1st Edition) [M]. Electronic Industry Press, 2019. (Optional)
[10] Gu Shengbao. Data Decision: Management, Analysis and Application of Enterprise Data (1st Edition) [M]. Electronic Industry Press, 2020. (Optional)
[2] MICK.SQL Basic Course [M]. Advanced SQL Course [M]. Posts and Telecom Press 2017. (Optional)
[3] Huang Jinhua. Getting started with MySQL is very simple [M]. Tsinghua University Press, 2011. (Optional)
[4] Stephenson, Jin Lao, Jones. SQL Introduction Classic (5th Edition) [M]. Posts and Telecom Press, 2011. (Optional)
[5] Jia Junping, He Xiaoqun, Jin Yongjin. Statistics (7th edition) [M]. China Renmin University Press, 2018. (Optional)
[6] Huang Chengming. Data Management (1st Edition) [M]. Electronic Industry Press, 2014. (Optional)
[7] Jingdong Data Innovation Group. Jingdong Platform Data Operation (1st Edition) [M]. Electronic Industry Press, 2016. (Optional)
[8] Liu Baohong, Zhao Ling, etc. The three lines of defense in the supply chain (1st Edition) [M]. Mechanical Industry Press 2020. (Optional)
[9] Chen Zhe. Utilizing data: data analysis to drive business (1st Edition) [M]. Electronic Industry Press, 2019. (Optional)
[10] Gu Shengbao. Data Decision: Management, Analysis and Application of Enterprise Data (1st Edition) [M]. Electronic Industry Press, 2020. (Optional)
CDA Certification Exam Committee
CDA Institute
CDA Institute