KASNEB DD32 — Data Management and Analytics | Theory Companion

A self-paced study companion for KASNEB DD32, with 30 lessons and 6 theory quizzes. Learn through explanations, worked examples, guided practice and answer checks.

What you will learn

• Define and validate data keys, types and transformations.
• Reproduce spreadsheet and R calculations.
• Explain distributed storage and processing concepts.
• Evaluate analytical outputs, quality and model limitations.

Course coverage

data management; Excel and R; data structures and quality; distributed storage and processing; analytical and machine-learning methods.

Practical study

A spreadsheet application and a permitted R environment for relevant exercises. Distributed-system lessons use conceptual worked examples and do not require a paid cluster. Use fictional data, preserve original inputs and retain calculations or code with independent checks. All worked-example inputs are supplied in the lessons.

Continue practical work at the DD32 Analytics Lab checkpoint, or use a permitted local calculation environment. Retain your own evidence files; opening a checkpoint is not a graded submission.

Assessment

Complete the topic checks and the eight-question final quiz. The final quiz allows three attempts, has a 20-minute limit and requires 70% to pass. Guided practical work is self-checked; this companion does not include instructor grading of uploaded practical files.

Official examination preparation

This is an independent study companion and does not award a KASNEB qualification or guarantee an examination result. Check the current syllabus and requirements for your sitting on the official KASNEB website. The course topic list is not a certification of complete current syllabus coverage.