LoadingLoadingLoadingLoadingMSc Data Science & Development Policy — one admission, a shared core, two named specialisations.
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A double-path master's for analysts in African statistics offices, ministries and think tanks. Shared core in data and policy analysis, then pathways in Data Science (modelling, geospatial, survey methods) and Development Policy (evaluation, budgeting, political economy), with a policy-analytics capstone. African Future University delivers this award fully online across African time zones. There is no campus. Assessment is identity-checked and the academic standard is the same for every student. 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 Science & Development Policy (Dual Track) is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 42. Students move from Statistics & Data Handling for Policy through Statistics & Data Handling for Policy; Programming for Data Analysis; Development Economics & Political Economy; Research Methods & Identity-Checked Assessment; Machine Learning & Predictive Modelling; Geospatial Analysis & Remote Sensing for Development, 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 African Future University awards. No professional-body logo is used in marketing to imply endorsement.
Study online
Tuition is free. Examinations are free. There is no exam fee. Study from anywhere in the world.
| Code | Course | Credits · hours |
|---|---|---|
| AFDP701 | Statistics & Data Handling for Policy Statistics & Data Handling for Policy (AFDP701) is a 3-credit course on MSc Data Science & Development 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.
| 3 · 30 h |
| AFDP702 | Programming for Data Analysis Programming for Data Analysis (AFDP702) is a 3-credit course on MSc Data Science & Development 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.
| 3 · 30 h |
| AFDP703 | Development Economics & Political Economy Development Economics & Political Economy (AFDP703) is a 3-credit course on MSc Data Science & Development 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 Development Economics & Political Economy 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.
| 3 · 30 h |
| AFDP704 | Research Methods & Identity-Checked Assessment Research Methods & Identity-Checked Assessment (AFDP704) is a 3-credit course on MSc Data Science & Development 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.
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| Code | Course | Credits · hours |
|---|---|---|
| AFDP705 | Machine Learning & Predictive Modelling Machine Learning & Predictive Modelling (AFDP705) is a 3-credit course on MSc Data Science & Development 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 Machine Learning & Predictive Modelling 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.
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| Code | Course | Credits · hours |
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| AFDP709 | Impact Evaluation & Causal Methods Impact Evaluation & Causal Methods (AFDP709) is a 3-credit course on MSc Data Science & Development 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.
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| Code | Course | Credits · hours |
|---|---|---|
| AFDP712 | Integrating Two Disciplines in Practice Integrating Two Disciplines in Practice (AFDP712) is a 3-credit course on MSc Data Science & Development 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.
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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.
The published duration is 24 months. Teaching language: English + French assessment options. Actual time-to-complete depends on mode and any recognised prior learning.
This is a fully online award. You study from your country. No student visa and no campus relocation are required.
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.
Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.
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.
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. African Future University publishes verification pages for certificates. We do not claim automatic equivalence in every country.
Start an application on this website. Progress is saved from the first step. Admissions: admissions@afutureuni.com.
Recognised & Accredited
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| 3 · 30 h |
| AFDP706 | Geospatial Analysis & Remote Sensing for Development Geospatial Analysis & Remote Sensing for Development (AFDP706) is a 3-credit course on MSc Data Science & Development 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 & Remote Sensing for Development 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.
| 3 · 30 h |
| AFDP707 | Survey Design & Administrative Data Survey Design & Administrative Data (AFDP707) is a 3-credit course on MSc Data Science & Development 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 Survey Design & Administrative 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.
| 3 · 30 h |
| AFDP708 | Data Visualisation & Communication Data Visualisation & Communication (AFDP708) is a 3-credit course on MSc Data Science & Development 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.
| 3 · 30 h |
| 3 · 30 h |
| AFDP710 | Public Budgeting & Expenditure Analysis Public Budgeting & Expenditure Analysis (AFDP710) is a 3-credit course on MSc Data Science & Development 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.
| 3 · 30 h |
| AFDP711 | Sector Policy Design & Implementation Sector Policy Design & Implementation (AFDP711) is a 3-credit course on MSc Data Science & Development 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 Sector Policy Design & 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.
| 3 · 30 h |
| 3 · 30 h |
| AFDP713 | Dual-Track Capstone: Policy Analytics Brief Dual-Track Capstone: Policy Analytics Brief (AFDP713) is a 6-credit course on MSc Data Science & Development 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.
| 6 · 60 h |