← Professional catalogueKASNEB · Diploma in Data Management and Analytics (DDMA)
DD32 — Data Management and Analytics
Data-management and analytics preparation using realistic synthetic analytical scenarios.
SYLLABUS
DatalytIQs practical mapping v1 — source KASNEB DDMA July 2021
Official syllabus detail has not yet been activated in DatalytIQs.
01
DD32.1 · Big Data Management Concepts
Frame big-data problems, structures and lifecycle decisions.
02
DD32.2 · Visualising Real-World Big Data Problems
Select and interpret visual approaches for real-world analytical problems.
03
DD32.3 · Statistical Tools for Big Data Analysis
Apply statistical reasoning and visual diagnostics to data.
04
DD32.4 · Managing Big Data Using R
Prepare, manipulate and summarise data using R-oriented workflows.
05
DD32.5 · Data Analytics Using R and Big-Data Tools
Apply R and big-data ecosystem concepts to analytical scenarios.
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
DD32.1 · Big Data Management Concepts
DD32-BD-01 · Big-Data Architecture Case
Given a synthetic high-volume service scenario, classify the data, identify the five Vs, design a lifecycle and justify an analytics architecture.
Evidence: architecture decision · lifecycle map · risk note
Mode: evidence only · dataset dd32_architecture_case_v1Assignment — Big-Data Architecture Case
Complete the R/big-data practical as an analytical assignment. Document the data or architecture decisions, show reproducible R-oriented or big-data reasoning, and translate the result into an operational recommendation.
Deliverables: architecture decision · lifecycle map · risk 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
DD32.2 · Visualising Real-World Big Data Problems
DD32-R-01 · Real-World Visualisation Design
Using a synthetic public-service dataset, specify and produce appropriate charts/dashboard views and explain the decision question each visual supports.
Evidence: R-oriented analysis plan · visual outputs · decision interpretation
Mode: evidence only · dataset dd32_visual_v1Assignment — Real-World Visualisation Design
Complete the R/big-data practical as an analytical assignment. Document the data or architecture decisions, show reproducible R-oriented or big-data reasoning, and translate the result into an operational recommendation.
Deliverables: R-oriented analysis plan · visual outputs · decision interpretation · 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 readiness03
DD32.3 · Statistical Tools for Big Data Analysis
Theory-linked topic; no separate practical evidence activity is required in this release.
04
DD32.4 · Managing Big Data Using R
DD32-R-02 · R Data Management Mission
Import, inspect, transform, summarise and export a synthetic dataset using an R-oriented workflow. Record commands or script and resulting data-quality decisions.
Evidence: R script or command log · processed dataset · data-quality note
Mode: evidence only · dataset dd32_r_management_v1Assignment — R Data Management Mission
Complete the R/big-data practical as an analytical assignment. Document the data or architecture decisions, show reproducible R-oriented or big-data reasoning, and translate the result into an operational recommendation.
Deliverables: R script or command log · processed dataset · data-quality 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 readiness05
DD32.5 · Data Analytics Using R and Big-Data Tools
DD32-R-03 · Analytics Pipeline and Spark Case
Design an analytical pipeline for a synthetic large-data scenario, assigning appropriate roles to storage, distributed processing and R-based analysis.
Evidence: pipeline design · technology rationale · analytical result interpretation
Mode: evidence only · dataset dd32_pipeline_v1Assignment — Analytics Pipeline and Spark Case
Complete the R/big-data practical as an analytical assignment. Document the data or architecture decisions, show reproducible R-oriented or big-data reasoning, and translate the result into an operational recommendation.
Deliverables: pipeline design · technology rationale · analytical result interpretation · 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.
Big-Data Architecture
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetR Analytical Reasoning
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetR Data Management
Evidence-based competency for the DatalytIQs practical preparation pathway.
No reviewed evidence yetR 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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Catalogue inclusion is not a claim of official endorsement or production readiness. DatalytIQs is an independent professional learning and examination-preparation platform.