Data Science Foundations

Course contents (13 parts)

1. 1

   Pre-course assessment

   Check your starting knowledge before learning

2. 2

   What is Data Science?

   Lesson video

3. 3

   Frame the question before choosing a method

   Define the user, decision, analysis unit, time period, and success criteria.

4. 4

   Understand the data

   Document sources, meanings, ownership, access, and quality checks.

5. 5

   Clean and prepare

   Use repeatable data-cleaning rules and preserve original data.

6. 6

   Explore and test hypotheses

   Explore patterns carefully and distinguish correlation from causation.

7. 7

   Communicate and choose the next step

   Communicate findings, limitations, and possible next steps.

8. 8

   Applied case study and evidence review

   Clean data without redefining missing values

9. 9
   Document0 points1 min

   Course study guide

   A practical guide to the course lessons, with Thai and English editions.

10. 10
    Document0 points1 min

    Practical workbook

    Course-based exercises for review and planning a small real-world trial.

11. 11
    Document0 points1 min

    Quick reference

    A concise reference to course topics and review points for use while working.

12. 12
    Document0 points1 min

    Implementation planner

    A template for connecting course topics to goals, owners, and evidence of results.

13. 13

    Post-course assessment

    8 scenario-based questions applying the lesson concepts.