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This course includes: 13 lessons · 4 supporting documents
Pre-course assessment
Check your starting knowledge before learning
What is Data Science?
Lesson video
Understand data structure
Big data can challenge volume, velocity, variety, or quality; row count does not guarantee value. Check source, frequency, units, and usage rights. Confirm keys and grain before joining tables to avoid double counting.
Choose a chart that fits the question
Charts should answer a question: bars compare categories, lines show trends, and scatter plots show relationships. Use honest axes and scales, units, time periods, and metric definitions.
Design for readability and accessibility
Remove decoration that adds no value, use headings and annotations to guide attention, and use color meaningfully without relying on color alone. Check contrast, text size, and small screens.
Design a dashboard
Organize visuals around viewers’ questions rather than source tables. Separate overview and detail, define metrics consistently, and show update date, owner, limitations, and filters.
Tell the data story honestly
Guide readers from a question to evidence and proposals. Separate findings from interpretation, and state sample size, collection method, time period, and uncertainty.
Applied case study and evidence review
Compare errors across differently sized groups
Course study guide
A practical guide to the course lessons, with Thai and English editions.
Practical workbook
Course-based exercises for review and planning a small real-world trial.
Quick reference
A concise reference to course topics and review points for use while working.
Implementation planner
A template for connecting course topics to goals, owners, and evidence of results.
Post-course assessment
An 8-question quiz to review your understanding.
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