Collecting, cleaning, and analyzing data to extract meaningful insights and support decision-making through statistical thinking and visualization.
Data analysis is discovering patterns in raw data to support business decisions. It spans collection, cleaning, exploratory analysis, statistical testing, visualization, and communication. The core is asking the right questions, finding answers through data, and connecting them to action.
You can open data in a spreadsheet or basic tool and understand its structure. You grasp the meaning of rows, columns, and fields, and can sort and filter data to find what you need. You use basic aggregation functions like averages and sums, and can create simple charts to visually represent data.
A 14-day structured practice guide for Data Analysis.
SFIA 9 defines data analytics competency across 7 levels (Level 2 Assist to Level 6 Lead), with autonomy and complexity criteria at each stage directly applicable to level boundary design.
Provides Entry-Level, Mid-Level, and Senior stages with Analytical/Technical track distinctions, reflecting career-stage competency differences applicable to level design.
Four-stage proficiency model (Awareness-Comprehension-Application-Influence) providing a progressive growth pathway applicable to level design and checklist authoring.
