Bachelor of Science in Data Science and Analytics
Every institution in Uganda now holds more data than it can interpret, and almost none of them can convert it into a decision. The gap is not modelling talent; it is the far scarcer ability to move from a messy administrative dataset to a d...
Employment thesis
What this programme is designed to produce
Forward publishes the destination of every programme before you enrol. If the thesis stops holding, the programme is reviewed and, where necessary, retired. Next review: 2031.
Roles
- Data scientist
- analytics engineer
- business intelligence lead
- decision scientist
- statistician
- monitoring and evaluation analyst
- credit and risk analyst
- growth and product analyst
- public sector data analyst
- survey and impact analyst.
Employer types
- Banks, insurers and mobile money operators
- telecommunications operators
- retail and FMCG
- development agencies and impact funds
- ministries and statistical agencies
- health systems
- agricultural value chain firms
- and consulting practices.
Target outcomes we hold ourselves to
- Positioned for immediate formal sector employment in Uganda and the region, with strong remote work demand internationally. Progression to the MSc in Applied Artificial Intelligence, the MSc in Data Engineering and Cloud Architecture, or professional actuarial and analytics certifications.
Compulsory · The Forward Core
How the Core works →Every Forward programme, including this one, carries the same five cross-cutting requirements. They are graded, not optional.
- AI Fluency
- Quantitative Reasoning
- Ethics and African Context
- Human-Advantage Skills
- Enterprise
Includes 12 months paid work-integrated learning
Am I eligible?
Check yourself against the published requirements
7 questions written specifically for Bachelor of Science in Data Science and Analytics. Every answer is checked against a requirement Forward has published, and the result tells you which page that requirement comes from. Indicative only, the admissions office decides on your full file.
What is the highest qualification you hold or are completing?
Curriculum structure
The same degree, in both pathways
Every Forward programme is published in both pathways: Path One, the term plan of two six-month terms, and Path Two, the block sequence of twelve four-week blocks. Same competencies, same assessments, same award. Toggle to see this programme in each.
Path One term plan shown
Path One · The Term Model
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 101 | AI Fluency I: How Intelligent Systems Work Both | 4 | 2-2-0 |
| AIC 110 | Programming for Data Science I Performance | 5 | 2-2-4 |
| AIC 111 | Foundations of Statistics and Probability Both | 5 | 3-2-2 |
| AIC 112 | Data Literacy, Sources and Ethics Objective | 4 | 3-2-0 |
| AIC 113 | Databases and Structured Query Both | 4 | 2-2-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 102 | Quantitative Reasoning and Evidence Both | 4 | 2-2-0 |
| AIC 130 | Programming for Data Science II Performance | 5 | 2-2-4 |
| AIC 131 | Statistical Inference and Regression Both | 5 | 3-2-2 |
| AIC 132 | Data Wrangling and Quality Engineering Performance | 4 | 1-0-6 |
| AIC 133 | Mathematics for Analytics Objective | 4 | 3-2-0 |
| COP 190 | Workplace Immersion (4 weeks, recess term) Performance | 0 | 0-0-0 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 201 | Ethics, Society and the African Context Both | 4 | 2-2-0 |
| AIC 210 | Machine Learning for Analytics Performance | 5 | 2-2-4 |
| AIC 211 | Causal Inference and Experimental Design Both | 5 | 3-2-2 |
| AIC 212 | Data Visualisation and Decision Communication Performance | 4 | 1-0-6 |
| AIC 213 | Survey Methods and Official Both | 4 | 2-2-2 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 202 | Human Advantage: Argument, Writing and Teams Both | 4 | 2-2-0 |
| COP 290 | Co-operative Education Placement I (6 months) Performance | 10 | 0-0-0 |
| AIC 230 | Time Series, Forecasting and Demand Analytics Both | 5 | 3-2-2 |
| AIC 231 | Analytics Engineering and Pipelines Performance | 3 | 1-0-4 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 301 | AI Fluency II: Building, Evaluating and Auditing Both | 4 | 2-2-0 |
| COP 390 | Co-operative Education Placement II (6 months) Performance | 10 | 0-0-0 |
| AIC 310 | Decision Science and Optimisation Both | 5 | 3-2-2 |
| AIC 311 | Sector Analytics Practicum (finance, health or agriculture) Performance | 3 | 1-0-4 |
| Code | Course | CU | Hours |
|---|---|---|---|
| FDC 302 | Enterprise, Employability and Financial Literacy Both | 4 | 2-2-0 |
| FDC 401 | AI Fluency III: Domain Deployment, Governance and Assurance Both | 3 | 1-2-0 |
| FDC 402 | Capability Portfolio and Day One Preparation Both | 3 | 1-2-0 |
| AIC 330 | Data Science Capstone: Real Client Decision Product Performance | 8 | 0-2-14 |
| AIC 331 | Governance, Privacy and Responsible Analytics Objective | 4 | 3-2-0 |
Total credit units: 132
Curriculum source
Forward University Curriculum Compendium
Assessment
Coursework + project + integrated exam
Work-integrated learning
12 months paid co-operative education
Your week at Forward
A typical week on Bachelor of Science in Data Science and Analytics
Path One, the Term Model: your own pace across a six-month term, one live session a week, short flexible lessons, and assessment on demand.
Monday
Tuesday
Wednesday
Thursday
Friday
Curriculum explorer
Drill into the degree, step by step
Choose a study path, then a year, then a term or block, then a course. Credit totals add up as you go, and every course shows what comes before it and what it unlocks.
Step 1, choose a year
Step 2, choose a term
TERM 1 (SIX MONTHS) | FORMERLY YEAR 1, SEMESTER 1
Year 1 · this step is worth 22 CU
Cumulative
22
Programme total
132
22 CU of 132 CU completed by the end of this step (17%)
Step 3, choose a course
Pick a course to see the detail
Credits, assessment, prerequisites and what each course unlocks later.
The twelve Forward capabilities
What this programme develops in you
Every Forward degree develops the same twelve cross-cutting capabilities. The profile below is the level this programme is designed to develop and verify by graduation, on the four-level scale in Part B2 of the Curriculum Compendium. Every claim above Level 2 is verified by someone other than the teaching lecturer.
- C1Intelligent Systems Fluency CriticalLevel 4 of 4, Leading
- C2Quantitative and Evidential Reasoning CriticalLevel 4 of 4, Leading
- C3Ethical and Contextual JudgementLevel 3 of 4, Independent
- C4Communication and PersuasionLevel 4 of 4, Leading
- C5Disciplinary Mastery CriticalLevel 4 of 4, Leading
- C6Technical Production and CraftLevel 3 of 4, Independent
- C7Problem Framing and Systems ThinkingLevel 4 of 4, Leading
- C8Collaboration and Multidisciplinary TeamingLevel 3 of 4, Independent
- C9Enterprise, Value and Commercial Literacy CriticalLevel 4 of 4, Leading
- C10Professional Conduct and Workplace PerformanceLevel 3 of 4, Independent
- C11Learning to Learn and Adaptive CapacityLevel 3 of 4, Independent
- C12Stewardship and Public ContributionLevel 3 of 4, Independent
Critical capabilities for this programme: Intelligent Systems Fluency · Quantitative and Evidential Reasoning · Disciplinary Mastery · Enterprise, Value and Commercial Literacy. A graduate cannot pass out of this programme below the stated level on any capability marked critical.
Programme Learning Outcomes
What you will be able to do
Each outcome is assessed, and each is tagged with the Forward capabilities it is verified against.
- PLO 1Acquire, clean, join, document and govern a real administrative or commercial dataset and state honestly what it can and cannot support.
- PLO 2Design and execute an appropriate statistical analysis including experimental and quasi experimental designs and quantify uncertainty correctly.
- PLO 3Distinguish correlation from causation in a real policy or commercial question and apply an appropriate causal identification strategy.
- PLO 4Build, evaluate and deploy predictive and forecasting models, and monitor them in operation.
- PLO 5Design visualisations and dashboards that change what a decision maker does, and defend them under challenge.
- PLO 6Write and defend a decision memorandum that converts analysis into a recommendation with costed consequences.
- PLO 7Identify measurement bias, sampling bias and algorithmic harm in a dataset or model, and specify the remedy.
- PLO 8Use intelligent tools to accelerate analysis while detecting and correcting the errors they introduce.
- PLO 9Deliver an analytical product for a real external client against a real deadline.
Why this programme exists
Every institution in Uganda now holds more data than it can interpret, and almost none of them can convert it into a decision. The gap is not modelling talent; it is the far scarcer ability to move from a messy administrative dataset to a defensible recommendation that a board, a ministry or a cooperative will act on. This programme is therefore built around decision quality rather than technique. Statistics and causal inference are taught early and heavily, because the most expensive analytical failures in the region are causal ones. Every course uses genuine Ugandan and African data — census, agricultural, clinical, mobile money, market and administrative — and every major assessment ends with a decision memorandum defended to a non-technical audience.
Admission requirements
UACE with two principal passes including Mathematics or Economics, plus UCE with five passes including Mathematics and English. A quantitative reasoning task is sat at admission. Alternative pathways: Foundation Year, diploma entry with advanced standing, mature age entry and recognition of prior learning, all governed by Part A5 and Part G. Online applicants additionally complete the compulsory Digital Readiness orientation under Part D6.
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