← Professional catalogueKASNEB · Diploma in Data Management and Analytics (DDMA)
DD31 — Python Data Visualisation
Python data-visualisation preparation linked to secure Analytics Lab practice.
SYLLABUS
DatalytIQs practical mapping v1 — source KASNEB DDMA July 2021
Official syllabus detail has not yet been activated in DatalytIQs.
01
DD31.1 · Foundations of Python Programming
Apply Python foundations to analytical tasks.
02
DD31.2 · Python Environment
Prepare and reason about Python execution environments.
03
DD31.3 · Data Operations in Python
Prepare, clean, transform and combine analytical data.
04
DD31.4 · Data Visualisation Using Python
Create and interpret fit-for-purpose analytical visualisations.
05
DD31.5 · Statistical Data Analysis
Apply descriptive and inferential analysis using Python.
PREPARATION PATH
Learn → Practise → Demonstrate → Review
Tutor LMS carries the theory layer. Analytics Lab carries practical activity, evidence and readiness. Python/R execution remains fail-closed until an isolated runner is available.
Academy Course
Linked to the Academy preparation course.
Exam Workspace
4 practical activities mapped to the paper.
Evidence pending
Readiness appears only after reviewed practical evidence and competency evidence exist.
ANALYTICS LAB PRACTICALS
Evidence-generating activities
Each activity is DatalytIQs-developed preparation work mapped to the syllabus source. It is not an official KASNEB examination question.
01
DD31.1 · Foundations of Python Programming
DD31-PY-01 · Python Foundations Diagnostic
Write a short Python analysis script that defines typed values, applies operators and produces a concise analytical summary. Submit source code and interpreted output.
Evidence: Python source · output summary · interpretation note
Mode: evidence only · dataset dd31_foundations_v1Assignment — Python Foundations Diagnostic
Complete the Python practical as a reproducible analytical assignment. Explain the problem, document the data preparation and analytical method, provide code evidence and interpret the results for a non-technical decision maker.
Deliverables: Python source · output summary · interpretation note · learner reflection · final recommendation
Marking: problem formulation and method 20% · technical accuracy and working 30% · evidence and reproducibility 20% · interpretation and recommendation 20% · professional presentation 10%
120 minutes · Pass threshold 70% · contributes to readiness02
DD31.2 · Python Environment
Theory-linked topic; no separate practical evidence activity is required in this release.
03
DD31.3 · Data Operations in Python
DD31-PY-02 · Clean and Reshape a Messy Dataset
Inspect a synthetic CSV, identify quality defects, clean missing/inconsistent values, derive fields, group observations and produce a reproducible cleaned table.
Evidence: cleaning script · clean dataset · quality log
Mode: evidence only · dataset dd31_messy_records_v1Assignment — Clean and Reshape a Messy Dataset
Complete the Python practical as a reproducible analytical assignment. Explain the problem, document the data preparation and analytical method, provide code evidence and interpret the results for a non-technical decision maker.
Deliverables: cleaning script · clean dataset · quality log · learner reflection · final recommendation
Marking: problem formulation and method 20% · technical accuracy and working 30% · evidence and reproducibility 20% · interpretation and recommendation 20% · professional presentation 10%
120 minutes · Pass threshold 70% · contributes to readiness04
DD31.4 · Data Visualisation Using Python
DD31-PY-03 · Build an Analytical Visual Story
Create fit-for-purpose plots from a synthetic performance dataset and explain why each visual answers the stated analytical question.
Evidence: visualisation code · three analytical graphics · interpretation brief
Mode: evidence only · dataset dd31_visual_story_v1Assignment — Build an Analytical Visual Story
Complete the Python practical as a reproducible analytical assignment. Explain the problem, document the data preparation and analytical method, provide code evidence and interpret the results for a non-technical decision maker.
Deliverables: visualisation code · three analytical graphics · interpretation brief · learner reflection · final recommendation
Marking: problem formulation and method 20% · technical accuracy and working 30% · evidence and reproducibility 20% · interpretation and recommendation 20% · professional presentation 10%
120 minutes · Pass threshold 70% · contributes to readiness05
DD31.5 · Statistical Data Analysis
DD31-PY-04 · Statistical Analysis in Python
Produce descriptive statistics, correlation and a simple regression analysis from a synthetic dataset; interpret assumptions, coefficients and limitations.
Evidence: analysis code · statistical output · interpretation brief
Mode: evidence only · dataset dd31_stats_v1Assignment — Statistical Analysis in Python
Complete the Python practical as a reproducible analytical assignment. Explain the problem, document the data preparation and analytical method, provide code evidence and interpret the results for a non-technical decision maker.
Deliverables: analysis code · statistical output · interpretation brief · learner reflection · final recommendation
Marking: problem formulation and method 20% · technical accuracy and working 30% · evidence and reproducibility 20% · interpretation and recommendation 20% · professional presentation 10%
120 minutes · Pass threshold 70% · contributes to readinessEVIDENCE & READINESS
Sign in to build an evidence profile
Readiness is evidence-derived, not a completion badge. Reviewed practical scores and competency evidence feed this dashboard.
Python Data Preparation
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetAnalytical Interpretation
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetStatistical Analysis
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetPython Visualisation
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetAn active paper enrolment is required before learner evidence is recorded.
STATUS CONTROL
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