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Data science sits at the intersection of statistics, computing, and domain expertise. This programme teaches you to frame business questions as analytical problems, wrangle messy real-world datasets, and communicate findings to non-technical audiences. Modules cover Bayesian inference, deep learning, NLP, and MLOps. You will work with public datasets from healthcare, finance, and climate science, building a portfolio that demonstrates end-to-end analytical capability.
Every module uses real datasets — never toy CSVs alone. You will document reproducible notebooks and present to a mock executive panel in the capstone. AFU partners with public-health NGOs for anonymised analytics projects.
Faculty spotlight
Dr. Priya Sharma, ex-lead data scientist at a global bank, teaches MLOps; Professor Daniel Mensah specialises in causal inference for policy evaluation.
Study online
Tuition is free. Examinations are free. There is no exam fee. Study from anywhere in the world.
| Code | Course | Credits · hours |
|---|---|---|
| DS501 | Statistics & Probability for Data Science Build the probability and inference toolkit behind every data-science claim: distributions, estimation, testing, regression and Bayesian reasoning, with honest uncertainty.
| 3 · 30 h |
| DS502 | Programming for Analytics (Python/R) Write clean, tested and reproducible analysis code in Python and R, from vectorised data handling to APIs, databases, notebooks and version control.
| 3 · 30 h |
| DS503 | Data Wrangling & SQL Query, join, clean, validate and reshape real data with SQL and dataframes, and build pipelines that keep data quality visible.
| 3 · 30 h |
| DS504 | Data Visualisation & Storytelling Design charts and dashboards grounded in perception research, show uncertainty honestly, and build a narrative a decision-maker can act on.
| 3 · 30 h |
| Code | Course | Credits · hours |
|---|---|---|
| DS505 | Machine Learning Fundamentals Frame learning problems, fit and regularise linear, tree and ensemble models, evaluate them properly and use unsupervised methods where labels are missing.
| 3 · 30 h |
| DS506 | Deep Learning & Neural Networks Train neural networks from first principles, then apply convolutional, sequence and attention models, transfer learning and sound training practice.
|
| Code | Course | Credits · hours |
|---|---|---|
| DS601 | MLOps & Production Systems Take models from notebook to production: versioning, experiment tracking, serving, monitoring for drift, retraining and responsible deployment.
| 3 · 30 h |
| DS602 | Big Data Technologies (Spark) Process data that does not fit on one machine with distributed storage, Spark, Spark SQL, streaming and MLlib, and reason about cost and performance.
|
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. 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
| 3 · 30 h |
| DS507 | Natural Language Processing Represent and model text, from tokenisation and embeddings to transformer language models, and evaluate NLP systems for accuracy and bias.
| 3 · 30 h |
| DS508 | Time Series & Forecasting Decompose, model and forecast time series with ARIMA, exponential smoothing and machine learning, and evaluate forecasts with honest intervals.
| 3 · 30 h |
| 3 · 30 h |
| DS603 | MSc Data Science Capstone An independent, supervised data-science project on a real dataset, from proposal and data governance to validated results, a report and a reproducible release.
| 6 · 60 h |