T/SDBDA 4-2020 Active Group standards

T/SDBDA 4-2020 Data Analyst Competency Assessment Standards

T/SDBDA 4-2020

Publish Date: 2020-12-11 Implement Date: 2020-12-11 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us
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Basic Information

Standard Code: T/SDBDA 4-2020
Standard Type: Group standards
Standard Status: Active
is_force_gb: no
CCS Name: Economic and cultural
ICS Name: Software development and system files
Publish Date: 2020-12-11
Implement Date: 2020-12-11

Scope

Scope: This standard specifies the terms and definitions, competency requirements, and evaluation methods for assessing the competencies of data analysts. This standard applies to the evaluation of data analysts' competencies. Main Technical Content: 4.1 Competency Requirements 4.1.1 Requirements for Graduates in Statistics, Finance, Computer Science, or Those with Over One Year of Experience in Big Data Fields. 4.1.2 Bachelor's Degree Graduates from Non-Computer-Related Majors with Over Two Years of Experience in Data Analysis-Related Industries. 4.2 Basic Qualities Requirements Data analysts should possess the following basic qualities, including but not limited to: a) Ability to analyze and process data statistically; b) Capacity for data collection and preprocessing; c) Skill in operating visualization tools; d) Strong logical thinking; e) Ability to conduct in-depth analysis and mining using multiple data sources; f) Basic knowledge of data statistics. 4.3 Business Knowledge Requirements The evaluation of knowledge (K) primarily includes: foundational knowledge, professional knowledge, and related knowledge, along with the level requirements for each type of knowledge, as shown in Table 1. 4.4 Skill Requirements The evaluation of skills (S) primarily includes: basic skills, professional skills, and soft skills, along with the level requirements for each type of skill, as shown in Table 2. 4.5 Experience Requirements The evaluation of experience (E) primarily includes: work experience, employment history, and career transition, along with the level requirements for each type of experience, as shown in Table 3. 5 Evaluation Methods 5.1 Comprehensive Competency Evaluation The comprehensive evaluation of data analysts' competencies is shown in Table 4. 5.2 Evaluation Process Employees are evaluated and assigned levels, with the evaluation results serving as a basis for competency development and career advancement.

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