← Professional catalogueKASNEB · Diploma in Data Management and Analytics (DDMA)

DD24 — Quantitative Modelling Skills

Quantitative modelling preparation with staged problem definition, formulation, computation and interpretation.

PAPERDD24Quantitative Modelling Skills

LEVELLevel IIOfficial verification status: pending

ASSESSMENTComputer-basedOfficial examination structure verification pending.

TOOLSPendingOnly verified requirements are labelled official.

SYLLABUS

DatalytIQs practical mapping v1 — source KASNEB DDMA July 2021

Official syllabus detail has not yet been activated in DatalytIQs.

DD24.1 · Quantitative Modelling Concepts

Formulate quantitative models from decision problems.

DD24.2 · Regression Modelling

Specify and interpret regression models.

DD24.3 · Linear Programming

Formulate and solve constrained optimisation problems.

DD24.4 · Simulation Modelling

Design and interpret simulation models under uncertainty.

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.

THEORY

Academy Course

Linked to the Academy preparation course.

PRACTICE

Exam Workspace

4 practical activities mapped to the paper.

READINESS

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.

DD24.1 · Quantitative Modelling Concepts

DD24-MOD-01 · Formulate a Quantitative Model

Translate a synthetic management decision into variables, assumptions, mathematical relationships and a target expression; justify the selected model family.

Evidence: model specification · assumption register · decision interpretation

Mode: guided calculation · dataset dd24_model_formulation_v1

Assignment — Formulate a Quantitative Model

Complete the modelling practical as a decision-science assignment. State assumptions, formulate the model, show the solution method, validate the result and interpret the recommended decision.

Deliverables: model specification · assumption register · 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 readiness

DD24.2 · Regression Modelling

DD24-MOD-02 · Regression Modelling Mission

Fit and interpret a regression model using a synthetic operational dataset. Assess model specification, coefficients, fit and statistical evidence.

Evidence: model calculations · regression output · interpretation and limitations

Mode: guided calculation · dataset dd24_regression_v1

Assignment — Regression Modelling Mission

Complete the modelling practical as a decision-science assignment. State assumptions, formulate the model, show the solution method, validate the result and interpret the recommended decision.

Deliverables: model calculations · regression output · interpretation and limitations · 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 readiness

DD24.3 · Linear Programming

DD24-MOD-03 · Optimise a Constrained Decision

Formulate a linear programme from a synthetic resource-allocation case, identify the feasible region or solver setup and interpret the optimum.

Evidence: LP formulation · solution evidence · management recommendation

Mode: guided calculation · dataset dd24_lp_v1

Assignment — Optimise a Constrained Decision

Complete the modelling practical as a decision-science assignment. State assumptions, formulate the model, show the solution method, validate the result and interpret the recommended decision.

Deliverables: LP formulation · solution evidence · management recommendation · 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 readiness

DD24.4 · Simulation Modelling

DD24-MOD-04 · Monte Carlo Risk Simulation

Construct a simulation plan for uncertain inputs, generate or describe repeated trials, summarise the outcome distribution and interpret decision risk.

Evidence: simulation specification · result distribution · risk interpretation

Mode: guided calculation · dataset dd24_simulation_v1

Assignment — Monte Carlo Risk Simulation

Complete the modelling practical as a decision-science assignment. State assumptions, formulate the model, show the solution method, validate the result and interpret the recommended decision.

Deliverables: simulation specification · result distribution · risk 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 readiness

EVIDENCE & 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.

MODEL-FORM

Model Formulation

Evidence-based competency for the DatalytIQs practical preparation pathway.

No reviewed evidence yet

OPTIMISE

Optimisation

Evidence-based competency for the DatalytIQs practical preparation pathway.

No reviewed evidence yet

REG-MODEL

Regression Modelling

Evidence-based competency for the DatalytIQs practical preparation pathway.

No reviewed evidence yet

SIMULATE

Simulation

Evidence-based competency for the DatalytIQs practical preparation pathway.

No reviewed evidence yet

An active paper enrolment is required before learner evidence is recorded.

STATUS CONTROL

PRACTICAL MAPPED DRAFT

Catalogue inclusion is not a claim of official endorsement or production readiness. DatalytIQs is an independent professional learning and examination-preparation platform.

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