Tier 2 · Senior Diploma
Data Analytics
An advanced, selective senior diploma engineered to produce the kind of analyst Africa has been importing — and the world has been waiting for.
- Code
- DAT 200
- Tier
- Senior Diploma
- Duration
- 17 months · 3 semesters
- Cohort 01
- 02 November 2026
- Delivery
- On-Campus · Waterloo
- Credential
- Senior Diploma in Data Analytics
What this diploma teaches is what cannot be taught from zero in six months: industrial-grade data cleaning, inferential statistics, production analytics engineering, cloud warehousing, machine learning for business, and the discipline to stand in front of a boardroom and hold the room. Three semesters, seventeen months, one trajectory — from a capable foundation graduate to a senior, globally employable analyst.
What you'll become
- Senior Data Analyst
- Analytics Engineer
- BI Engineer
- Product Analyst
- Strategy Analyst
- Growth Analyst
- Decision Intelligence Specialist
- Reporting Lead
- Analytics Consultant
- Operations Intelligence Analyst
- Data Operations Specialist
- Founding Data Hire
§ 01 · The difference
What sets this programme apart.
No Vibe Coding
Every line of code a WIATech graduate writes, they can defend. Every query they ship, they understand. No copy-paste fluency. If you cannot explain it to a junior, you cannot use it on a project.
The Oral Defence
Every major project ends in an oral defence in front of faculty and external evaluators. You will stand, explain your choices, and answer hard questions — the way doctorates are awarded, and the way the boardrooms our graduates enter expect.
Project-First, Always
Forty percent of your grade comes from projects; twenty-five percent from capstones. Theory exists in service of the project. By graduation you will have built warehouses, dashboards, pipelines, and forecasting systems that work in production.
African Problems, Global Standards
We solve real problems from real organisations — Sierra Leonean banks, pan-African telcos, global NGOs, regional fintechs. The data is messy, the constraints are real, and the standard of rigour is global. There is no 'local discount' on quality.
AI-Augmented, Not AI-Replaced
Our graduates use AI as a force multiplier — prompt engineering, AI-assisted querying, automated reporting. But they also know exactly when AI is wrong, where it hallucinates, and why their human judgement is the asset, not the assistant.
Built for the Boardroom
Communication is taught every semester. Executive presentations are graded; stakeholder simulations are routine. Our graduates do not just produce charts — they produce decisions, and they hold their ground with executives, investors, and ministers.
§ 02 · Who this is for
Built for engineers ready to operate at depth.
◆ The Ambitious Graduate
Recently completed secondary or tertiary studies and looking for a career that pays, scales, and travels. You do not need a STEM background. You do need to clear the Tier 1 floor — by assessment or coursework. What you bring is discipline.
◆ The Career-Changer
Working in banking, telecoms, NGOs, government, or operations — and ready to become the analyst your organisation relies on. If the gate finds gaps, the foundation courses close them.
◆ The Self-Taught Builder
You already write SQL on weekends and build dashboards in Power BI. The technical assessment is built for you — clear it, walk into Semester 01. You need the structure, the credential, and the peer group to compound what you already know.
◆ The Founder & Operator
You run a small business, a department, or a startup, and you are tired of making decisions in the dark. This programme gives you the analytical operating system your venture needs — provided you can clear the Tier 1 floor on arrival.
§ 03 · The Tier 1 floor
What you need before you start.
This is an advanced programme that begins above the foundation floor. Clear it by passing the WIATech technical assessment — or by completing the matching Tier 1 Foundations courses. The assessment is a placement instrument, never a rejection.
Required foundations
This diploma opens with advanced SQL from week one — window functions, CTEs, warehouse modelling. Without the basics already in place, the advanced material lands on empty ground.
Python becomes the analyst's primary instrument from Semester 01 — Pandas, NumPy, automation, production pipelines. We do not stop to teach variables and loops here; that work belongs in FND 60.
Combines the field orientation every analyst needs — the analytics lifecycle, industry domains, data ethics — with spreadsheet-engineering depth (Excel, Power Query, Power Pivot, dashboarding). The pre-diploma floor for analysts.
Universal parallel co-requisite
Every WIATech student takes ENG 70 in parallel, regardless of tier or entry route. Communication is practised continuously: executive writing, briefing structure, presentation craft, and stakeholder communication.
Each gated prerequisite can be satisfied by passing the WIATech technical assessment or by completing the corresponding Tier 1 course. The assessment is a placement mechanism, not a rejection mechanism. For applicants whose assessment surfaces a quantitative-reasoning gap, WIATech may route them to a supplementary quantitative-foundations course before DAT 200 entry, determined case by case in the engineering interview.
Direct entry
Applicants who pass the technical assessment across SQL, Python, and analyst foundations enter DAT 200 directly at Semester 01. No Tier 1 coursework required; ENG 70 runs in parallel from week one.
Routed entry
Applicants whose assessment surfaces gaps in any of the three gated prerequisites are routed to the specific Tier 1 courses they need — and only those. This is a placement decision, not a remedial track. Both routes converge on the same diploma and the same standard.
§ 04 · The architecture
3 semesters. One graduating engineer.
Year 01 · Core · Semester 01
Core Analytics Engineering
The semester where students stop being learners and start being analysts. SQL moves from the basics certified at Tier 1 into the working language of every serious business, Python becomes the analyst's primary instrument, and statistics moves from descriptive into inference. Outcome: Junior Analyst, Operational.
SQL for Data Analytics
Beyond the SELECT-and-JOIN basics established in FND 40, straight into window functions, recursive queries, CTEs, and query optimisation. Students leave able to sit a senior SQL interview with confidence.
PostgreSQL · MySQL · Window Functions · CTEs · Query Optimisation
Data Cleaning & Preparation
Real-world data is messy, broken, and lying about itself. Industrial-grade cleaning workflows: missing data, outliers, standardisation, profiling, auditing, and multi-source merging.
Python · Pandas · Data Profiling · ETL Concepts
Python for Data Analytics
Python becomes the analyst's primary instrument. NumPy, Pandas, Matplotlib, Plotly. Aggregation, feature engineering, time-series visualisation, automation, and scheduled reporting.
NumPy · Pandas · Matplotlib · Plotly · Automation
Statistics for Data Analysts
The module that separates an average analyst from an elite one. Inferential statistics, hypothesis testing, confidence intervals, sampling, correlation, regression basics, and probability — taught as a way of seeing the world.
Descriptive · Inferential · Hypothesis Testing · Regression Basics · Probability
Data Visualisation & Storytelling
Visualisation is psychology, not decoration. Human perception, dashboard design, colour theory, and information hierarchy — applied in Power BI, Tableau, and Looker Studio.
Power BI · Tableau · Looker Studio · Dashboard Design · Storytelling
Capstone
Enterprise Analytics Simulation
Students operate as a full analytics department inside a simulated enterprise — telecom churn, banking fraud trends, NGO donor analytics, or e-commerce performance — running the entire cycle from database to executive presentation.
Deliverables: SQL database analysis · Python data processing · BI dashboard · Executive presentation · Technical documentation
Year 01 · Advanced · Semester 02
Production Analytics Engineering
Students stop thinking like analysts and start thinking like analytics engineers — the people who build the systems other analysts depend on. Warehousing, ETL, advanced SQL engineering, production Python, executive BI, forecasting, and decision science. Outcome: Mid-Level Analytics Engineer.
Advanced SQL & Analytical Engineering
Beyond querying, into engineering. Recursive queries, materialised views, stored procedures, star/snowflake schema modelling, query cost analysis, and enterprise batch workflows.
PostgreSQL · BigQuery · Snowflake · Star / Snowflake Schema
Data Warehousing & ETL Engineering
How modern organisations actually move data — OLTP vs OLAP, data marts, warehouse modelling, ETL/ELT pipelines, orchestration with DAGs, governance and lineage.
Apache Airflow · dbt · SQL · Python · Data Governance
Advanced Python for Analytics Engineering
Production-quality code — advanced Pandas, vectorisation, memory optimisation, scheduled workflows, REST API integration, web data collection, and parallel processing.
Pandas · Polars · Requests · BeautifulSoup · Multiprocessing
Business Intelligence Engineering
Students become elite dashboard and KPI architects — executive KPI systems, self-service analytics, drill-through reporting, north-star metrics, and C-suite dashboards.
Power BI · Tableau · Looker Studio · KPI Frameworks
Statistical Analysis & Decision Science
Advanced analytical reasoning — multivariate analysis, ANOVA, regression, time-series forecasting, experimental design, and scenario modelling.
Time-Series · Regression · ANOVA · Scenario Modelling
Executive Storytelling & Visualisation Psychology
How elite analysts influence decisions — visual cognition, dashboard psychology, executive narratives, boardroom communication, and information architecture.
Visual Cognition · Executive Narratives · Information Architecture
Capstone
Enterprise Intelligence System
Students build a complete analytics environment for a simulated enterprise — the full stack: pipeline, warehouse, modelling, dashboards, forecasting, and executive reporting.
Deliverables: ETL pipeline · Data warehouse · SQL modelling · BI dashboards · Forecasting models · Executive reporting system
Year 02 · Senior · Semester 03
Modern, AI & Cloud Analytics
The final semester bridges advanced analytics with the AI-augmented, cloud-native, experiment-driven future of the profession — machine learning, big data, cloud platforms, product analytics, A/B testing, and consulting — closing with a 3-6 month industry internship and the flagship capstone. Outcome: Senior, Globally Employable.
Machine Learning for Data Analysts
How analysts use ML in business — supervised and unsupervised learning, regression, decision trees, clustering — applied to churn, fraud detection, sales forecasting, and recommendation, with proper evaluation.
Scikit-learn · Supervised & Unsupervised Learning · Model Evaluation
Big Data Foundations
How modern organisations handle data at scale — distributed systems, data lakes, columnar storage, Apache Spark fundamentals, and batch and streaming processing.
Apache Spark · Hadoop Concepts · Data Lakes · Streaming
Cloud Analytics Engineering
Modern cloud analytics infrastructure — BigQuery, AWS Redshift, Azure Synapse, cloud ETL and serverless analytics, plus cloud security, IAM, and governance.
BigQuery · AWS Redshift · Azure Synapse · IAM
Product Analytics & User Behaviour Analysis
Retention, engagement, conversion, and activation metrics; funnel and drop-off analysis; cohort analysis; event tracking and product instrumentation — the analytical core of every modern SaaS and consumer business.
Funnel Analytics · Cohorts · Event Tracking · Product Metrics
A/B Testing & Experimentation
Hypothesis design, test setup, randomisation, statistical evaluation, significance testing, and bias detection — the empirical foundation of modern decision-making.
Hypothesis Design · Significance Testing · Bias Detection
Analytics Consulting & Business Strategy
How analysts influence organisations strategically — business problem framing, stakeholder analysis, strategic recommendations, and industry analytics across banking, telecom, healthcare, NGOs, and government.
Consulting Thinking · Strategic Frameworks · Client Communication
Capstone
The AI-Powered Business Intelligence Platform
Students spend the closing months building a production-grade enterprise analytics platform — and defending it before instructors, industry professionals, and external evaluators. The institute's flagship graduation requirement and the centrepiece of every graduate's portfolio.
Deliverables: Cloud-hosted warehouse · Automated ETL · Machine-learning integration · Executive dashboard system · Forecasting engine · Product analytics layer · Strategic business report
§ 05 · The toolkit
The stack you'll master.
§ 06 · Grading
How the work is measured.
§ 07 · Credentials & career
What you walk out with.
A Tier 2 Senior Diploma in Data Analytics, issued by WIATech and marked with the graduate's specialisation, accompanied by the verified portfolio that defines a WIATech analyst.
The portfolio
- Microsoft Certified: Power BI Data Analyst Associate preparation
- Google Data Analytics Professional Certificate preparation
- Tableau Desktop Specialist & Certified Data Analyst preparation
- AWS Certified Data Analytics — Specialty (fundamentals)
- dbt Analytics Engineering Certification preparation
- SQL and Python proficiency examinations
Career acceleration
- A mandatory 3-6 month real industry internship
- Portfolio engineering & GitHub polishing
- SQL, case, and product-analytics interview prep
- LinkedIn optimisation & thought leadership
- Remote work, freelancing & international collaboration
- Direct introduction to partner organisations
- Alumni network access — for life
§ 08 · Admissions
Who we admit. How we admit them.
Admission does not depend on credentials — no specific WASSCE results, university degree, or certifications are required. What is required is demonstrated capability and clearing the Tier 1 floor: the three gated prerequisites — FND 40, FND 60, and DAT 45 — cleared before Semester 01 by passing the WIATech technical assessment or by completing the corresponding Tier 1 courses, with ENG 70 in parallel. No STEM background is needed, and strong portfolios are evaluated favourably regardless of formal background.
Application
Online application form, academic records, and a 300-word statement of intent.
Technical Assessment
A WIATech-administered assessment across SQL, Python, analyst foundations, and quantitative reasoning. Applicants who pass clear the Tier 1 floor directly; applicants with gaps are placed in the specific Tier 1 courses they need.
Engineering Interview
A structured interview with faculty, evaluating discipline, intent, and fit — and confirming the placement route surfaced by the assessment.
Offer & Placement
Successful applicants receive a formal offer specifying their entry route — direct DAT 200 entry, or the Tier 1 courses required first — with their enrolment package and onboarding schedule.
Starting from the foundations, via the Data Analytics Foundations Pathway: NLe 60,500 total (NLe 10,500 foundations + NLe 50,000 diploma). Tier 2 Senior Diploma tuition is set in advance and paid in monthly instalments after a seat deposit. Full tuition & payment →