MSc

MSc Data Analytics & Public Policy (Dual Track)

MSc Data Analytics & Public Policy — one admission, a shared core, two named specialisations.

Online & New Capital Campus

4 Semesters (2 Years)42 creditsEnglish and Arabic (bilingual assessment; academic Arabic required for AR-medium tracks)

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MSc Data Analytics & Public Policy (Dual Track)

Overview

A double-path master's for analysts in Arab-region ministries, statistics offices and think tanks. Shared core in analytics and policy, then pathways in Data Analytics (modelling, dashboards, geospatial) and Public Policy (evaluation, budgeting, delivery), with a policy-analytics capstone. Imperial University in Cairo delivers this award fully online across the Arab region. There is no campus. English is the administrative language of the award; Arabic-medium tracks require additional language evidence. Assessment is identity-checked. This is an academic Master's award; where a field has a licensing or registration route, that remains a matter for your professional body or regulator. You will work through the material as a practitioner-researcher: applied methods, real or anonymised cases, and a supervised capstone or dissertation as the final component. Faculty mark whether you noticed the constraints a slide deck usually hides. MSc Data Analytics & Public Policy (Dual Track) is taught as one award, not a list of unrelated modules. The published length is 4 Semesters (2 Years), and the credit total is 42. Students move from Statistics & Data Handling for Policy through Statistics & Data Handling for Policy; Programming for Data Analysis; Public Policy Processes in the Arab States; Research Methods & Identity-Checked Assessment; Predictive Modelling & Machine Learning; Government Dashboards & Performance Data, and finish on Dual-Track Capstone: Policy Analytics Brief. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Turn a messy business question into a defensible analysis Choose, fit and validate the right statistical or ML model Build dashboards and pipelines that other people can trust and maintain Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

The programme carries 42 credits across four assessed modules. Delivery is tuition-free aside from the examination fee. Transcripts use letter grades and a GPA scale consistent with Imperial University in Cairo awards. No professional-body logo is used in marketing to imply endorsement.

Who This Programme Is For

Analysts and reporting staff moving into modelling
Managers who must run a data or insight function
Professionals in any sector who want to work with data credibly
Graduates with some quantitative background

How you can study

Digital only

Study online

100% tuition-free online

Online tuition is completely free. Study from anywhere in Egypt, the Arab region, and worldwide with full digital flexibility.

✦ Tuition: £0 / $0
✦ Examinations are free. There is no exam fee.
✦ Learn from anywhere · Live online seminars

Apply online →

What You Will Learn

Turn a messy business question into a defensible analysis
Choose, fit and validate the right statistical or ML model
Build dashboards and pipelines that other people can trust and maintain
Communicate uncertainty honestly to decision-makers

Curriculum

Term 1 — Shared Data & Policy Core

Code	Course	Credits · hours
ALDP701	Statistics & Data Handling for Policy Statistics & Data Handling for Policy (ALDP701) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Statistics & Data Handling for Policy as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP701-M1 · The question, the unit of analysis, and data quality — Statistics & Data Handling for Policy ALDP701-M2 · Features, baselines, and error — Statistics & Data Handling for Policy ALDP701-M3 · Validation, leakage, and what the model must not decide — Statistics & Data Handling for Policy ALDP701-M4 · Monitoring, drift, and human review — Statistics & Data Handling for Policy ALDP701-M5 · Interpretation for a non-technical decision maker — Statistics & Data Handling for Policy ALDP701-M6 · A supervised project with method, result, and limitation — Statistics & Data Handling for Policy	3 · 30 h
ALDP702	Programming for Data Analysis Programming for Data Analysis (ALDP702) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Programming for Data Analysis as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP702-M1 · The question, the unit of analysis, and data quality — Programming for Data Analysis ALDP702-M2 · Features, baselines, and error — Programming for Data Analysis ALDP702-M3 · Validation, leakage, and what the model must not decide — Programming for Data Analysis ALDP702-M4 · Monitoring, drift, and human review — Programming for Data Analysis ALDP702-M5 · Interpretation for a non-technical decision maker — Programming for Data Analysis ALDP702-M6 · A supervised project with method, result, and limitation — Programming for Data Analysis	3 · 30 h
ALDP703	Public Policy Processes in the Arab States Public Policy Processes in the Arab States (ALDP703) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Public Policy Processes in the Arab States as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP703-M1 · The question, the unit of analysis, and data quality — Public Policy Processes in the Arab States ALDP703-M2 · Features, baselines, and error — Public Policy Processes in the Arab States ALDP703-M3 · Validation, leakage, and what the model must not decide — Public Policy Processes in the Arab States ALDP703-M4 · Monitoring, drift, and human review — Public Policy Processes in the Arab States ALDP703-M5 · Interpretation for a non-technical decision maker — Public Policy Processes in the Arab States ALDP703-M6 · A supervised project with method, result, and limitation — Public Policy Processes in the Arab States	3 · 30 h
ALDP704	Research Methods & Identity-Checked Assessment Research Methods & Identity-Checked Assessment (ALDP704) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Research Methods & Identity-Checked Assessment as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP704-M1 · The question, the unit of analysis, and data quality — Research Methods & Identity-Checked Assessment ALDP704-M2 · Features, baselines, and error — Research Methods & Identity-Checked Assessment ALDP704-M3 · Validation, leakage, and what the model must not decide — Research Methods & Identity-Checked Assessment ALDP704-M4 · Monitoring, drift, and human review — Research Methods & Identity-Checked Assessment ALDP704-M5 · Interpretation for a non-technical decision maker — Research Methods & Identity-Checked Assessment ALDP704-M6 · A supervised project with method, result, and limitation — Research Methods & Identity-Checked Assessment	3 · 30 h

Term 2 — Pathway A: Data Analytics

Code	Course	Credits · hours
ALDP705	Predictive Modelling & Machine Learning Predictive Modelling & Machine Learning (ALDP705) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Predictive Modelling & Machine Learning as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP705-M1 · The question, the unit of analysis, and data quality — Predictive Modelling & Machine Learning ALDP705-M2 · Features, baselines, and error — Predictive Modelling & Machine Learning ALDP705-M3 · Validation, leakage, and what the model must not decide — Predictive Modelling & Machine Learning ALDP705-M4 · Monitoring, drift, and human review — Predictive Modelling & Machine Learning ALDP705-M5 · Interpretation for a non-technical decision maker — Predictive Modelling & Machine Learning ALDP705-M6 · A supervised project with method, result, and limitation — Predictive Modelling & Machine Learning	3 · 30 h
ALDP706	Government Dashboards & Performance Data Government Dashboards & Performance Data (ALDP706) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Government Dashboards & Performance Data as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP706-M1 · The question, the unit of analysis, and data quality — Government Dashboards & Performance Data ALDP706-M2 · Features, baselines, and error — Government Dashboards & Performance Data ALDP706-M3 · Validation, leakage, and what the model must not decide — Government Dashboards & Performance Data ALDP706-M4 · Monitoring, drift, and human review — Government Dashboards & Performance Data ALDP706-M5 · Interpretation for a non-technical decision maker — Government Dashboards & Performance Data ALDP706-M6 · A supervised project with method, result, and limitation — Government Dashboards & Performance Data	3 · 30 h
ALDP707	Geospatial Analysis for Public Services Geospatial Analysis for Public Services (ALDP707) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Geospatial Analysis for Public Services as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP707-M1 · The question, the unit of analysis, and data quality — Geospatial Analysis for Public Services ALDP707-M2 · Features, baselines, and error — Geospatial Analysis for Public Services ALDP707-M3 · Validation, leakage, and what the model must not decide — Geospatial Analysis for Public Services ALDP707-M4 · Monitoring, drift, and human review — Geospatial Analysis for Public Services ALDP707-M5 · Interpretation for a non-technical decision maker — Geospatial Analysis for Public Services ALDP707-M6 · A supervised project with method, result, and limitation — Geospatial Analysis for Public Services	3 · 30 h
ALDP708	Data Visualisation & Communication Data Visualisation & Communication (ALDP708) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Data Visualisation & Communication as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP708-M1 · The question, the unit of analysis, and data quality — Data Visualisation & Communication ALDP708-M2 · Features, baselines, and error — Data Visualisation & Communication ALDP708-M3 · Validation, leakage, and what the model must not decide — Data Visualisation & Communication ALDP708-M4 · Monitoring, drift, and human review — Data Visualisation & Communication ALDP708-M5 · Interpretation for a non-technical decision maker — Data Visualisation & Communication ALDP708-M6 · A supervised project with method, result, and limitation — Data Visualisation & Communication	3 · 30 h

Term 3 — Pathway B: Public Policy

Code	Course	Credits · hours
ALDP709	Impact Evaluation & Causal Methods Impact Evaluation & Causal Methods (ALDP709) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Impact Evaluation & Causal Methods as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP709-M1 · The question, the unit of analysis, and data quality — Impact Evaluation & Causal Methods ALDP709-M2 · Features, baselines, and error — Impact Evaluation & Causal Methods ALDP709-M3 · Validation, leakage, and what the model must not decide — Impact Evaluation & Causal Methods ALDP709-M4 · Monitoring, drift, and human review — Impact Evaluation & Causal Methods ALDP709-M5 · Interpretation for a non-technical decision maker — Impact Evaluation & Causal Methods ALDP709-M6 · A supervised project with method, result, and limitation — Impact Evaluation & Causal Methods	3 · 30 h
ALDP710	Public Budgeting & Expenditure Analysis Public Budgeting & Expenditure Analysis (ALDP710) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Public Budgeting & Expenditure Analysis as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP710-M1 · The question, the unit of analysis, and data quality — Public Budgeting & Expenditure Analysis ALDP710-M2 · Features, baselines, and error — Public Budgeting & Expenditure Analysis ALDP710-M3 · Validation, leakage, and what the model must not decide — Public Budgeting & Expenditure Analysis ALDP710-M4 · Monitoring, drift, and human review — Public Budgeting & Expenditure Analysis ALDP710-M5 · Interpretation for a non-technical decision maker — Public Budgeting & Expenditure Analysis ALDP710-M6 · A supervised project with method, result, and limitation — Public Budgeting & Expenditure Analysis	3 · 30 h
ALDP711	Delivery Units & Implementation Delivery Units & Implementation (ALDP711) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Delivery Units & Implementation as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP711-M1 · The question, the unit of analysis, and data quality — Delivery Units & Implementation ALDP711-M2 · Features, baselines, and error — Delivery Units & Implementation ALDP711-M3 · Validation, leakage, and what the model must not decide — Delivery Units & Implementation ALDP711-M4 · Monitoring, drift, and human review — Delivery Units & Implementation ALDP711-M5 · Interpretation for a non-technical decision maker — Delivery Units & Implementation ALDP711-M6 · A supervised project with method, result, and limitation — Delivery Units & Implementation	3 · 30 h

Code

Course

Credits · hours

ALDP709

Impact Evaluation & Causal Methods

Impact Evaluation & Causal Methods (ALDP709) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Impact Evaluation & Causal Methods as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

ALDP709-M1 · The question, the unit of analysis, and data quality — Impact Evaluation & Causal Methods
ALDP709-M2 · Features, baselines, and error — Impact Evaluation & Causal Methods
ALDP709-M3 · Validation, leakage, and what the model must not decide — Impact Evaluation & Causal Methods
ALDP709-M4 · Monitoring, drift, and human review — Impact Evaluation & Causal Methods
ALDP709-M5 · Interpretation for a non-technical decision maker — Impact Evaluation & Causal Methods
ALDP709-M6 · A supervised project with method, result, and limitation — Impact Evaluation & Causal Methods

3 · 30 h

ALDP710

Public Budgeting & Expenditure Analysis

Public Budgeting & Expenditure Analysis (ALDP710) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Public Budgeting & Expenditure Analysis as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

ALDP710-M1 · The question, the unit of analysis, and data quality — Public Budgeting & Expenditure Analysis
ALDP710-M2 · Features, baselines, and error — Public Budgeting & Expenditure Analysis
ALDP710-M3 · Validation, leakage, and what the model must not decide — Public Budgeting & Expenditure Analysis
ALDP710-M4 · Monitoring, drift, and human review — Public Budgeting & Expenditure Analysis
ALDP710-M5 · Interpretation for a non-technical decision maker — Public Budgeting & Expenditure Analysis
ALDP710-M6 · A supervised project with method, result, and limitation — Public Budgeting & Expenditure Analysis

3 · 30 h

ALDP711

Delivery Units & Implementation

Delivery Units & Implementation (ALDP711) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Delivery Units & Implementation as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

ALDP711-M1 · The question, the unit of analysis, and data quality — Delivery Units & Implementation
ALDP711-M2 · Features, baselines, and error — Delivery Units & Implementation
ALDP711-M3 · Validation, leakage, and what the model must not decide — Delivery Units & Implementation
ALDP711-M4 · Monitoring, drift, and human review — Delivery Units & Implementation
ALDP711-M5 · Interpretation for a non-technical decision maker — Delivery Units & Implementation
ALDP711-M6 · A supervised project with method, result, and limitation — Delivery Units & Implementation

3 · 30 h

Term 4 — Integration & Dual-Track Capstone

Code	Course	Credits · hours
ALDP712	Integrating Two Disciplines in Practice Integrating Two Disciplines in Practice (ALDP712) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Integrating Two Disciplines in Practice as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice ALDP712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice ALDP712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice ALDP712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice ALDP712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice ALDP712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice	3 · 30 h
ALDP713	Dual-Track Capstone: Policy Analytics Brief Dual-Track Capstone: Policy Analytics Brief (ALDP713) is a 6-credit course on MSc Data Analytics & Public Policy (Dual Track), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Dual-Track Capstone: Policy Analytics Brief as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. ALDP713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Policy Analytics Brief ALDP713-M2 · Features, baselines, and error — Dual-Track Capstone: Policy Analytics Brief ALDP713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Policy Analytics Brief ALDP713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Policy Analytics Brief ALDP713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Policy Analytics Brief ALDP713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Policy Analytics Brief	6 · 60 h

Code

Course

Credits · hours

ALDP712

Integrating Two Disciplines in Practice

Integrating Two Disciplines in Practice (ALDP712) is a 3-credit course on MSc Data Analytics & Public Policy (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Integrating Two Disciplines in Practice as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

ALDP712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice
ALDP712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice
ALDP712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice
ALDP712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice
ALDP712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice
ALDP712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice

3 · 30 h

ALDP713

Dual-Track Capstone: Policy Analytics Brief

Dual-Track Capstone: Policy Analytics Brief (ALDP713) is a 6-credit course on MSc Data Analytics & Public Policy (Dual Track), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Dual-Track Capstone: Policy Analytics Brief as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

ALDP713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Policy Analytics Brief
ALDP713-M2 · Features, baselines, and error — Dual-Track Capstone: Policy Analytics Brief
ALDP713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Policy Analytics Brief
ALDP713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Policy Analytics Brief
ALDP713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Policy Analytics Brief
ALDP713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Policy Analytics Brief

6 · 60 h

Career Paths

Data analyst / senior data analyst
Data scientist
Analytics engineer
Business intelligence lead
Analytics or insight manager

Programme Highlights

SQL, statistics, modelling and visualisation end to end
Experimentation and causal inference, not just correlation
A capstone on a real or anonymised dataset
Portfolio-ready deliverables

Assessment

Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.

Admission requirements

Academic entry

Bachelor's degree from a recognised institution (minimum 2:2 / GPA 2.5/4.0 or an equivalent national scale)
Identity document for remote identity-checked assessment enrolment

English language

English proficiency: IELTS 5.5, TOEFL 46 or equivalent (waived for prior English-medium study). Arabic-medium tracks require additional academic-Arabic evidence as stated on the programme page.

Documents

Statement of Purpose (500 words minimum) covering goals, professional context and fit
Two academic or professional references with verifiable contact details
CV or résumé covering education and relevant work
Official transcripts (digital copies accepted at application; certified copies before award)

Programme questions

How long is the MSc Data Analytics & Public Policy (Dual Track)?

The published duration is 4 Semesters (2 Years). Teaching language: English and Arabic (bilingual assessment; academic Arabic required for AR-medium tracks). Actual time-to-complete depends on mode and any recognised prior learning.

Can I study this from my country?

You may study this award fully online from your country, or — where published — on campus at IUC. Online study does not require a student visa. Campus study may.

What does it cost?

Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.

What are the entry requirements?

Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.

How is the programme assessed?

Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.

Will this award be recognised where I live?

Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. Imperial University in Cairo publishes verification pages for certificates. We do not claim automatic equivalence in every country.

How do I apply?

Start an application on this website. Progress is saved from the first step. Admissions: admissions@iucairo.co.uk.

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