CODING & DATA ANALYTICS CERTIFICATIONS (12 CERTIFICATIONS)
All coding and data analytics certifications are aligned to ACM/IEEE CS2023, W3C, Cambridge International, AP Computer Science, and industry standards from Meta, Google, AWS, Microsoft, CompTIA, Python Institute, IBM SPSS, StataCorp STATA, and QSR NVivo. Curriculum is reviewed every 6–12 months given the rapid pace of change in technology.
Who coding and data analytics is for?
Children aged 7–12 (CJC) · Teenagers aged 13–17 (CTWD) · Adults aged 18+ — all backgrounds welcome. The CDA certification is specifically designed for non-programmers (business professionals, NGO staff, government officers, researchers) who need powerful data skills using Excel, Power BI, Tableau, SQL, Google Sheets, STATA, SPSS, R, NVivo, and Generative AI.
CJC — Certified Junior Coder (CJC)
“Every expert was once a beginner. Start here.”
An internationally benchmarked introductory coding certification for children — covering digital literacy, computational thinking, Scratch visual programming, and an introduction to Python text coding. Aligned to CSTA K–8 and Cambridge Primary Computing standards.
| Programme Details | Information |
|---|---|
|
Level |
Children | Ages 7–12 |
|
Audience |
Primary school children, homeschooled learners, and young beginners with no prior coding experience |
|
Standards |
CSTA K–8 Computer Science Standards · Cambridge Primary Computing · Common Core Mathematical Practices for Computational Thinking |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Scratch or Python project submission + oral or video presentation (minimum 75%) |
|
Certificate |
CJC Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
How computers work: hardware, software, input, and output devices · Internet safety, digital citizenship, and responsible online behaviour · Files, folders, and organising digital work · Typing skills and essential keyboard shortcuts · Using productivity tools: Google Docs, Slides, and Sheets
Decomposition: breaking big problems into small, manageable steps · Pattern recognition: identifying patterns in data and problems · Abstraction: focusing on what matters, ignoring unnecessary detail · Algorithms: writing step-by-step instructions for a computer · Debugging: strategies for finding and fixing mistakes in code
Scratch interface: sprites, backdrops, costumes, and sounds · Motion, looks, and sound blocks · Events and control: loops, conditionals, and broadcast messaging Variables: storing and using information in programs · Sensing and operators: making programs respond to users
Designing a game concept: rules, goals, and user experience · Adding scoring systems, lives, levels, and timers · Character animation: smooth movement and costume switching · User interaction: keyboard controls and mouse input · Sharing and presenting projects to an audience
Why text coding? Moving from Scratch blocks to real code · Python basics: print(), input(), variables, and data types · Arithmetic operations and string manipulation · Simple decisions: if/else conditional statements · Mini project: building an interactive greeting program
Design brief: plan your project using a storyboard or flowchart · Build: construct your Scratch game or Python program · Test and debug: find and fix all errors systematically · Document: write a simple README explaining what the program does · Present: demonstrate your project to peers and instructor with a live demo
Apply computational thinking to structure and solve real problems · Build interactive games and animations in Scratch · Write basic Python programs using variables, loops, and conditionals · Present and explain a digital project confidently · Demonstrate readiness for the Certified Teen Web Developer (CTWD) pathway
Complete all 6 modules, submit a completed Scratch game or Python program, and deliver a short oral or video presentation explaining the project (minimum 75%).
Foundation for all coding career pathways. Builds computational thinking applicable in every future career. Prepares for CTWD.
CTWD — Certified Teen Web Developer (CTWD)
“Build websites the world will see.”
A comprehensive web development certification for teenagers aligned to W3C standards, Cambridge IGCSE Computer Science, and AP CSP. Students design, build, and deploy real live websites from scratch.
| Programme Details | Information |
|---|---|
|
Level |
Teenagers | Ages 13–17 |
|
Audience |
Secondary school students, young aspiring developers, and teen hobbyists wanting to build real websites |
|
Standards |
W3C Web Standards · Cambridge IGCSE Computer Science · AP Computer Science Principles (College Board) · WCAG 2.1 Web Accessibility Standards |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Deployed live multi-page responsive website + code review session with instructor (minimum 75%) |
|
Certificate |
CTWD Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
Internet architecture: clients, servers, HTTP/HTTPS, and DNS · Domain names, web hosting, and how browsers render pages · Browser developer tools: inspecting and debugging live pages · Web standards, accessibility (WCAG 2.1), and inclusive design · Setting up VS Code and a professional developer workflow
Document structure and semantic HTML5 elements · Headings, paragraphs, lists, links, images, and embedded media · Tables, forms, input types, and validation attributes · HTML5 landmarks: header, nav, main, section, article, footer · SEO fundamentals: meta tags and descriptive page titles
Selectors, specificity, the cascade, and CSS custom properties · Box model: margin, border, padding, and content areas · Typography, colour systems, and background properties · Flexbox: one-dimensional flexible layout system · CSS Grid: two-dimensional complex page layouts · Responsive design: media queries and mobile-first approach
Variables, data types, operators, and type coercion · Functions: declarations, expressions, arrow functions, and scope · DOM manipulation: selecting, modifying, and creating elements · Events: click, keypress, form submit, and custom event listeners · Fetch API: loading and displaying live data from external APIs · ES6+ features: destructuring, spread, template literals, and modules
Git fundamentals: init, add, commit, diff, log, and status · GitHub: repositories, remote origins, push, pull, and clone · Branching: feature branches, merging, and pull requests · Resolving merge conflicts and collaborative coding etiquette · Deploying live websites: GitHub Pages and Netlify
Plan: sitemap, wireframes, and content strategy · Design: colour palette, typography, and UI layout decisions · Build: multi-page responsive site with JavaScript interactions · Optimise: compress images, improve performance, accessibility audit · Deploy: publish live on the internet with a custom domain
Build semantic, accessible, and responsive websites using HTML5 and CSS3 · Add JavaScript interactivity and fetch live data from APIs · Use Git and GitHub for version control and deployment · Deploy live websites to the internet on custom domains · Achieve readiness for Certified Full-Stack Web Developer (CFWD)
Complete all 6 modules, deploy a live multi-page responsive website, and pass a code review session with an instructor (minimum 75%).
Junior Web Developer, Freelance Web Designer, Frontend Developer (entry), UI Designer, and pathway to CFWD Full-Stack Developer.
CPD — Certified Python Developer (CPD)
“Python: the world’s most versatile language. Master it.”
A professional Python certification aligned to Python Institute PCEP and PCAP standards and ACM CS2023 guidelines — covering Python from fundamentals through OOP, data structures, file handling, and data analysis automation.
| Programme Details | Information |
|---|---|
|
Level |
University Students & Adults | Ages 18+ |
|
Audience |
University students, career changers, analysts, engineers, researchers, and working professionals |
|
Standards |
Python Institute PCEP & PCAP Standards · ACM CS2023 Curriculum Guidelines · IEEE Computer Society Standards |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Documented GitHub project submission + live instructor code review (minimum 75%) |
|
Certificate |
CPD Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
Python ecosystem: installation, IDEs, virtual environments, and pip · Variables, data types, type conversion, and operators · String methods, f-string formatting, and string slicing · Input and output: command-line interaction and basic I/O · PEP 8 style guide: writing clean, readable, professional Python code
Conditional statements: if, elif, else, and ternary expressions · Loops: for, while, break, continue, pass, and else on loops · Functions: parameters, default arguments, *args, and **kwargs · Lambda functions, map(), filter(), and reduce() · Recursion: base cases, call stack, and practical examples
Lists: indexing, slicing, comprehensions, and common methods · Tuples: immutability, packing, unpacking, and named tuples · Dictionaries: creation, methods, comprehensions, and nesting · Sets: mathematical operations, frozensets, and use cases · Choosing the right data structure: time and space complexity basics
Classes and objects: attributes, methods, and __init__ constructor · Inheritance: single, multiple, and method resolution order · Polymorphism, encapsulation, and abstraction · Magic/dunder methods: __str__, __repr__, __len__, __eq__ · Design patterns: singleton, factory, observer — introduction
Reading and writing: text files, CSV, JSON, and XML · Exception handling: try, except, else, finally, and raise · Creating custom exception classes and hierarchies · Python standard library: os, sys, datetime, math, random, re · Creating, importing, and distributing custom Python modules
NumPy: arrays, vectorised operations, and broadcasting · Pandas: DataFrames, groupby, merge, pivot, and apply · Matplotlib and Seaborn: publication-quality visualisations · Web scraping: BeautifulSoup and Requests libraries · Automation: files, emails, APIs, and scheduled tasks
Choose your track: data analysis, automation, or web scraping · Project planning: scope, architecture, and milestone timeline · Build: complete Python project with clean, documented code · GitHub: README, requirements.txt, and full version control · Code review: live session with written feedback from instructor
Write clean, professional Python code following PEP 8 and OOP principles · Build, test, and document Python applications across multiple domains · Analyse data with NumPy and Pandas and create professional visualisations · Automate tasks and work with external APIs, files, and web data · Achieve a credential benchmarked against Python Institute PCEP and PCAP standards
Complete all 7 modules, submit a documented Python project on GitHub with README and requirements.txt, and pass a live code review (minimum 75%).
Python Developer, Backend Developer, Data Analyst, Automation Engineer, Research Programmer, and pathway to CDA Data Analyst and CDS Data Scientist.
CDA — Certified Data Analyst (CDA)
★ Why this certification was added: Data analytics is one of the most in-demand skills globally and is distinct from data science. It serves business professionals, government workers, NGO staff, administrators, and students who need to work confidently with data without becoming programmers. The tools covered — Excel, Power BI, Tableau, SQL, Google Sheets, STATA, SPSS, and Generative AI — are used daily by millions of professionals worldwide. This certification bridges the gap between mathematics and advanced data science, making the coding track accessible to a much wider audience.
“Turn numbers into decisions. Every professional needs this skill.”
A practical, internationally benchmarked data analytics certification covering Microsoft Excel (advanced), Power BI, Tableau, SQL, Google Sheets, STATA, SPSS, applied statistics, and Generative AI for data analytics — designed for business professionals, government workers, NGO staff, researchers, and students who need to analyse, visualise, and communicate data confidently without advanced programming knowledge.
| Programme Details | Information |
|---|---|
|
Level |
University, Professional & Advanced Secondary — suitable for anyone with basic computer skills |
|
Audience |
Business analysts, government officers, NGO staff, administrators, researchers, teachers, accountants, public health workers, project managers, and any professional who works with data |
|
Standards |
Microsoft Power BI Data Analyst Associate (PL-300) · Tableau Desktop Specialist · Google Data Analytics Certificate · StataCorp STATA Standards · EViews Econometric Standards · IBM SPSS Statistics Standards · R Project for Statistical Computing · QSR NVivo Standards · OECD Data Literacy Framework · ACM Data Science Curriculum (Analytics Track) · OpenAI & Anthropic AI for Analytics Standards |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored online examination (minimum 75%) + data analysis project submitted with dashboard, SPSS/STATA/R output, and written report |
|
Certificate |
CDA Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What is data analytics? The role of a data analyst in organisations · Types of analytics: descriptive, diagnostic, predictive, and prescriptive · The data analytics workflow: collect, clean, analyse, visualise, communicate · Types of data: quantitative vs qualitative, structured vs unstructured · Data sources: primary data, secondary data, open data, and databases · Data ethics: privacy, consent, confidentiality, and responsible data use · Introduction to all tools: Excel, Power BI, Tableau, SQL, Google Sheets, STATA, SPSS, and AI · Setting up your analytics environment: installing and configuring all tools
Data organisation: tables, named ranges, and structured data in Excel · Advanced formulas: VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP, IF, IFS, SUMIFS, COUNTIFS · PivotTables: creating, customising, filtering, grouping, and slicers · PivotCharts: building dynamic charts linked to PivotTables · Data cleaning in Excel: removing duplicates, blanks, text-to-columns, TRIM, and CLEAN · Conditional formatting: highlighting patterns, heat maps, and data bars · Power Query: importing, transforming, and combining data from multiple sources · What-if analysis: Goal Seek, Scenario Manager, and Data Tables · Excel dashboards: combining charts, slicers, and KPIs into a single executive view · Hands-on lab: build a complete sales performance dashboard from a raw dataset
Google Sheets vs Excel: key differences and when to use each · Importing data: CSV, Excel, Google Forms, and connected data sources · Advanced functions: QUERY function, ARRAYFORMULA, IMPORTRANGE, and FILTER · Collaborative features: sharing, commenting, version history, and permissions · Charts and visualisations: building and customising charts in Google Sheets · Google Sheets dashboards: linking multiple sheets into summary views · Data validation and dropdown lists: ensuring data quality in shared sheets · Automating tasks with Google Apps Script: introduction and basic macros · Connecting Google Sheets to Looker Studio (Google Data Studio) for reporting · Hands-on lab: build a collaborative project tracker with automated summary dashboard
What is SQL and why every data analyst must know it · Database concepts: tables, rows, columns, primary keys, and foreign keys · Basic SQL: SELECT, FROM, WHERE, ORDER BY, LIMIT, and DISTINCT · Filtering and conditions: AND, OR, NOT, IN, BETWEEN, LIKE, and IS NULL · Aggregate functions: COUNT, SUM, AVG, MIN, MAX, GROUP BY, and HAVING · Joining tables: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN · Subqueries and CTEs (Common Table Expressions): readable, layered queries · Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and running totals · Data cleaning with SQL: handling NULLs, deduplication, and type casting · Hands-on lab: analyse a real-world business database — answer 20 analytical questions using SQL
Descriptive statistics: mean, median, mode, range, variance, and standard deviation · Data distributions: normal, skewed, and bimodal — identifying and interpreting · Percentiles, quartiles, and the interquartile range (IQR) · Correlation: Pearson and Spearman correlation coefficients — interpretation and limitations · Inferential statistics: samples, populations, and sampling bias · Hypothesis testing: null hypothesis, p-values, and significance levels · Chi-square test: testing relationships between categorical variables · t-tests: comparing means between two groups (independent and paired samples) · Regression analysis: simple and multiple linear regression — interpreting coefficients · Probability basics: rules, conditional probability, and Bayes’ theorem · Hands-on lab: conduct a full statistical analysis on a real dataset — test a hypothesis and interpret results
Introduction to SPSS: interface, variable view, data view, and output viewer · Importing data into SPSS: CSV, Excel, and text files · Defining variables: variable names, labels, value labels, missing values, and measurement levels · Data cleaning in SPSS: finding and handling missing values, outliers, and errors using Explore · Descriptive statistics in SPSS: Frequencies, Descriptives, and Explore procedures · Cross-tabulations and chi-square test: Crosstabs procedure — interpretation of output · Comparing means: Independent samples t-test, paired samples t-test, and one-sample t-test · One-way ANOVA: testing differences across three or more groups with post-hoc tests · Correlation in SPSS: Pearson and Spearman correlation — interpreting the output table · Linear regression in SPSS: simple and multiple regression — R², coefficients, and significance · Non-parametric tests: Mann-Whitney U, Kruskal-Wallis, and Wilcoxon signed-rank tests · Reliability analysis: Cronbach’s Alpha for scale and survey data validation · Factor analysis: introduction to exploratory factor analysis (EFA) for survey data · Exporting SPSS output: tables and charts to Word, Excel, and PDF · Hands-on lab: complete a full research data analysis in SPSS — from import to final output tables
Introduction to STATA: interface, do-files, log files, and basic navigation · Importing data into STATA: CSV, Excel, and other formats · Data management: labelling variables, recoding, generating new variables, and sorting · Descriptive statistics in STATA: summarize, tabulate, codebook, and inspect commands · Data cleaning in STATA: missing values, outliers, and duplicate observations · Cross-tabulations: tabulate and tab2 commands · Correlation and regression in STATA: correlate, regress, and output interpretation · t-tests and ANOVA in STATA: ttest, oneway, and interpreting p-values · Graphs in STATA: histogram, scatter plot, bar chart, box plot, and twoway graphs · Exporting STATA outputs: tables and graphs to Word and Excel · Panel data in STATA: xtset, xtreg — fixed effects and random effects models · Introduction to EViews: interface, workfile structure, and importing time-series data · Descriptive statistics and graphing in EViews: series, groups, and basic plots · Unit root tests in EViews: ADF, PP, and KPSS tests for stationarity · Ordinary Least Squares (OLS) regression in EViews: estimation and diagnostic tests · Time series analysis in EViews: ARIMA, ACF, PACF, and Box-Jenkins methodology · VAR (Vector Autoregression) in EViews: model specification and Granger causality test · Cointegration in EViews: Engle-Granger and Johansen cointegration tests · GARCH models in EViews: volatility modelling for financial and economic data · Forecasting in EViews: in-sample fit, out-of-sample forecast, and forecast evaluation · Hands-on lab: complete a time-series analysis in EViews — unit root, OLS, ARIMA, and forecast
Data visualisation principles: choosing the right chart for the right message · Colour theory, typography, and layout for professional dashboards · Power BI fundamentals: Power BI Desktop, data sources, and the Power BI service · Connecting data in Power BI: Excel, SQL, CSV, web, and SharePoint sources · Data modelling in Power BI: relationships, star schema, and calculated columns · DAX basics: SUM, AVERAGE, CALCULATE, FILTER, RELATED, and time intelligence functions · Building Power BI reports: visuals, slicers, drill-through, and bookmarks · Power BI dashboards: pinning visuals, setting alerts, and sharing with stakeholders · Tableau fundamentals: Tableau Desktop, connecting data, and the interface · Building Tableau visualisations: bar, line, scatter, map, heat map, and tree map · Tableau calculated fields: basic formulas and level-of-detail (LOD) expressions · Tableau dashboards and stories: layout, actions, filters, and Tableau Public publishing · Power BI vs Tableau: when to use each tool and industry context · Hands-on lab: build identical dashboards in both Power BI and Tableau from the same dataset
How Generative AI is transforming the data analytics profession · Using ChatGPT, Claude, and Gemini to accelerate data analysis workflows · AI-assisted data cleaning: prompting AI to identify errors, suggest fixes, and generate cleaning scripts · Writing and debugging SQL with AI: generating queries, explaining errors, and optimising performance · Using AI to generate Excel formulas and Power Query transformations from plain English descriptions · AI-powered exploratory data analysis: asking AI to suggest analytical approaches for a dataset · Generating data visualisation code and chart recommendations using AI tools · Using AI to interpret statistical outputs: explaining regression results, p-values, and ANOVA tables in plain language · Building AI-assisted data reports: drafting insights, narratives, and executive summaries from raw findings · Microsoft Copilot in Excel and Power BI: using built-in AI features for analytics · Google Duet AI in Google Sheets and Looker Studio: AI-powered analysis and reporting · Prompt engineering for data analytics: writing effective prompts to get accurate, usable analytical outputs · Ethics and limitations of AI in analytics: hallucinations, data privacy, and over-reliance on AI outputs · Hands-on lab: complete a full data analysis project using AI tools at every stage — cleaning, analysis, visualisation, and reporting
Why R matters for researchers: reproducibility, open-source, and academic credibility · R and RStudio setup: interface, projects, packages, and the tidyverse ecosystem · Data import in R: readr, haven (SPSS/STATA files), and readxl for Excel · Data wrangling with dplyr: filter, select, mutate, group_by, summarise, and joins · Data cleaning in R: handling NAs, outliers, recoding, and data type conversion · Data visualisation with ggplot2: scatter, bar, line, box, histogram, facets, and themes · Statistical analysis in R: t-tests, ANOVA, chi-square, correlation, and linear regression · Survey data analysis in R: srvyr and survey packages for weighted analysis · R Markdown: producing fully reproducible reports combining code, output, and narrative · Introduction to NVivo: interface, project structure, sources, nodes, and cases · Importing qualitative data into NVivo: transcripts, PDFs, Word documents, and survey responses · Coding in NVivo: free nodes, tree nodes, auto-coding, and in-vivo coding · Node queries and matrix coding: exploring patterns and cross-tabulating codes by attributes · Visualisations in NVivo: word clouds, cluster analysis, tree maps, and mind maps · Exporting NVivo outputs: coding summaries, matrices, and charts to Word and Excel · Integrating R and NVivo: combining quantitative R analysis with qualitative NVivo findings in mixed methods research · Hands-on lab: conduct a mixed methods analysis — R for quantitative survey data, NVivo for qualitative interview data — and write an integrated findings section |
Analyse and clean datasets using Excel, Google Sheets, SQL, SPSS, STATA, and R · Build professional interactive dashboards in Power BI and Tableau · Apply descriptive and inferential statistics to real-world analytical problems · Conduct time-series analysis and econometric modelling using EViews · Write SQL queries from basic to advanced window functions and CTEs · Code and analyse qualitative data in NVivo and integrate with quantitative R findings · Use Generative AI to accelerate data cleaning, analysis, visualisation, and reporting · Communicate data findings clearly through written reports and visual dashboards · Achieve a credential aligned to Microsoft PL-300, Tableau, IBM SPSS, STATA, EViews, R, NVivo, and Google Data Analytics standards |
Complete all 10 modules, pass a proctored online examination (minimum 75%), and submit a complete data analysis project including a cleaned dataset, an interactive dashboard, statistical outputs from at least two tools (SPSS, STATA, R, or EViews), and a 1,500-word written report of findings.
Data Analyst, Business Analyst, Research Analyst, Monitoring & Evaluation Officer, Business Intelligence Analyst, Econometrician, Operations Analyst, Public Health Data Analyst, Government Data Officer, NGO Programme Analyst, AI-Augmented Data Analyst. Average starting salary: $45,000–$80,000 USD. |
CFWD — Certified Full-Stack Web Developer (CFWD)
“From pixel to database — own the entire stack.”
A rigorous, industry-aligned full-stack development certification covering React, Node.js, databases, DevOps, testing, and software engineering — aligned to Meta, Google Developer, W3C, and ACM CS2023 standards.
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
|
Audience |
Aspiring full-stack developers, software engineers, computer science graduates, and developers upgrading their skills |
|
Standards |
Meta Developer Standards · Google Developer Certification · W3C Web Standards · ACM CS2023 Curriculum · AWS Certified Developer |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Deployed production-ready full-stack application with CI/CD + technical interview simulation (minimum 75%) |
|
Certificate |
CFWD Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
React fundamentals: JSX, components, props, and state · Hooks: useState, useEffect, useContext, useReducer, useMemo, useCallback · React Router v6: navigation, dynamic routing, and protected routes · State management: Context API and Redux Toolkit with RTK Query · Performance: memoisation, code splitting, and lazy loading |
Advanced CSS: animations, transitions, and CSS custom properties · Tailwind CSS: utility-first design system and configuration · Component libraries: Material UI, Shadcn/UI, or Radix UI · Accessibility (a11y): ARIA roles, keyboard navigation, and focus management · Design systems: building and documenting reusable component libraries |
Node.js: event loop, streams, workers, and module system · Express.js: routing, middleware, error handling, and MVC architecture · RESTful API design: conventions, versioning, and Swagger documentation · Authentication: JWT, refresh tokens, bcrypt, OAuth 2.0, and Passport.js · API security: rate limiting, CORS, Helmet, and input validation with Zod |
SQL with PostgreSQL: advanced queries, joins, transactions, and views · Database design: normalisation, schema design, and ER diagrams · NoSQL with MongoDB: documents, collections, indexing, and aggregation · ORMs: Prisma for SQL and Mongoose for MongoDB · Optimisation: query planning, indexing strategies, and caching with Redis |
Git advanced: rebasing, cherry-picking, tags, and Git flow branching · Docker: images, containers, volumes, networks, and Docker Compose · CI/CD pipelines: GitHub Actions for automated testing and deployment · Cloud deployment: AWS (EC2, S3, RDS) or Railway/Render · 12-factor app principles and secrets management |
Unit testing: Jest and Vitest — mocks, spies, and test coverage · Integration and API testing: Supertest and Postman collections · End-to-end testing: Cypress and Playwright · Software engineering: SOLID principles, DRY, KISS, and YAGNI · Agile methodology: user stories, sprints, standups, and retrospectives |
System design: architecture, database schema, and API design · Build: complete frontend + backend with all features · Testing: full test suite — unit, integration, and end-to-end · Deploy: production-ready on cloud with CI/CD pipeline and monitoring · Demo day: present to a panel of technical reviewers with live Q&A |
Build complete full-stack applications using React, Node.js, and SQL/NoSQL databases · Design and implement secure RESTful APIs with authentication and authorisation · Deploy applications to cloud infrastructure with Docker and CI/CD pipelines · Write comprehensive test suites and apply professional software engineering principles · Achieve a credential benchmarked against Meta, Google Developer, and ACM CS2023 standards |
Complete all 7 modules, deploy a production-ready full-stack application with a CI/CD pipeline, and pass a technical interview simulation (minimum 75%). |
Full-Stack Developer, Software Engineer, Frontend Engineer, Backend Engineer, API Developer. Average starting salary: $65,000–$110,000 USD. |
CDS — Certified Data Scientist (CDS)
“Turn data into decisions. Turn decisions into impact.” A comprehensive data science certification aligned to ACM Data Science standards, IBM Professional Data Science, and Google Data Analytics — covering the complete data science lifecycle from wrangling through machine learning to MLOps deployment. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
|
Audience |
Analysts, researchers, engineers, business professionals, and postgraduate students moving into data science |
|
Standards |
ACM Data Science Curriculum · IBM Professional Data Science · Google Data Analytics · J-PAL Statistical Standards |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Deployed end-to-end data science project + technical report (minimum 75%) |
|
Certificate |
CDS Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
Data science lifecycle: problem definition through to actionable insight · Types of data, sources, public APIs, and open datasets · Ethics in data science: bias, fairness, privacy, and GDPR compliance · Tools setup: Python, Jupyter, Anaconda, and VS Code · SQL for data retrieval: SELECT, JOINs, GROUP BY, and subqueries |
Data collection: APIs, web scraping, and public datasets · Data cleaning: missing values, duplicates, outliers, and imputation · Exploratory data analysis: distributions, correlations, and patterns · Feature engineering: encoding, scaling, and transformation · Pandas advanced: groupby, merge, pivot, apply, and method chaining |
Descriptive statistics and common probability distributions · Inferential statistics: hypothesis testing, p-values, and effect sizes · Confidence intervals, power analysis, and sample size calculation · Bayesian statistics: Bayes’ theorem, prior/posterior, and MCMC intro · A/B testing: design, analysis, and interpretation |
Regression: linear, polynomial, ridge, lasso, and elastic net · Classification: logistic regression, KNN, decision trees, and SVM · Ensemble methods: random forest, gradient boosting, XGBoost, LightGBM · Model evaluation: accuracy, precision, recall, F1, ROC-AUC · Hyperparameter tuning: Grid Search, Random Search, and Optuna |
Clustering: K-Means, DBSCAN, hierarchical, Gaussian mixture models · Dimensionality reduction: PCA, t-SNE, UMAP, and autoencoders · Anomaly detection: isolation forest and one-class SVM · Recommender systems: collaborative and content-based filtering · Deep learning introduction: ANNs, CNNs, RNNs with TensorFlow/Keras |
Matplotlib and Seaborn: publication-quality statistical visualisations · Plotly and Dash: interactive web-based dashboards · Tableau or Power BI: executive-level business intelligence dashboards · Storytelling with data: choosing the right chart and building a narrative · Presenting findings to non-technical audiences with clarity and impact |
Model serialisation: pickle, joblib, and ONNX formats · Building ML APIs with FastAPI: endpoints, validation, and async · Deploying models: Hugging Face Spaces, AWS SageMaker, or GCP Vertex AI · Model monitoring: data drift, concept drift, and performance degradation · MLflow: experiment tracking, model registry, and reproducibility |
End-to-end project on a real-world dataset of your choice · Problem definition, EDA, feature engineering, modelling, and evaluation · Deployed interactive dashboard or REST API endpoint · Technical report: methodology, results, limitations, and recommendations · Panel presentation with Q&A from data science reviewers |
Execute the full data science lifecycle from collection to model deployment · Apply supervised and unsupervised ML to real-world datasets · Build and deploy ML APIs and interactive dashboards · Communicate insights compellingly to technical and non-technical audiences · Achieve a credential benchmarked against ACM, IBM, and Google Data Analytics standards |
Complete all 8 modules, deploy an end-to-end data science project with an interactive dashboard or API, and submit a technical report (minimum 75%). |
Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, AI Researcher, Quantitative Analyst. Average starting salary: $75,000–$120,000 USD. |
CCP — Certified Cloud Practitioner (CCP)
★ Why this certification was added: Cloud computing powers over 65% of the world’s internet infrastructure. AWS, Azure, and GCP are used by virtually every technology company globally. Without cloud skills, coding graduates are not fully job-ready in 2025 and beyond. Aligned to AWS CLF-C02, Google ACE, and Microsoft Azure AZ-900 — three of the most sought-after technology credentials on Earth.
“Own the cloud. Own your career.” A globally benchmarked cloud computing certification covering cloud fundamentals, AWS core services, cloud security, architecture best practices, DevOps, and cost optimisation — with hands-on labs deploying real applications on AWS infrastructure. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
|
Audience |
Developers, IT professionals, engineers, business analysts, and students entering technology careers |
|
Standards |
AWS Certified Cloud Practitioner (CLF-C02) · Google Associate Cloud Engineer · Microsoft Azure Fundamentals (AZ-900) · NIST Cloud Computing Framework · Cloud Security Alliance (CSA) CCM |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored online examination (minimum 75%) + hands-on cloud deployment project submitted via GitHub |
|
Certificate |
CCP Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What is cloud computing? History and the shift from on-premise infrastructure · Cloud service models: IaaS, PaaS, SaaS, and FaaS with real-world examples · Cloud deployment models: public, private, hybrid, and multi-cloud · Economics of cloud: CapEx vs OpEx, pay-as-you-go, and total cost of ownership · Major providers: AWS, Azure, GCP — strengths, market share, and use cases · Shared responsibility model: provider vs customer responsibilities · Global infrastructure: regions, availability zones, and edge locations |
Compute: EC2 instances, Auto Scaling Groups, and Elastic Load Balancing · Serverless: AWS Lambda — functions, triggers, and event-driven architecture · Managed platforms: Elastic Beanstalk, ECS, and EKS (Kubernetes) · Storage: S3 — buckets, versioning, lifecycle policies, and storage classes · Block and file storage: EBS volumes, EFS, and FSx · Networking: VPC — subnets, route tables, internet gateways, NAT gateways · CDN and DNS: CloudFront content delivery and Route 53 routing · Hands-on lab: launch EC2, configure VPC, host a static website on S3 |
Relational databases: RDS — MySQL, PostgreSQL, Multi-AZ, read replicas · Amazon Aurora: performance, serverless, and global database features · NoSQL: DynamoDB — tables, partition keys, global secondary indexes · Caching: ElastiCache (Redis and Memcached) · Messaging: SQS queues, SNS pub/sub, and EventBridge routing · Containers: ECS task definitions, ECR registry, Fargate serverless · API management: API Gateway — REST/HTTP APIs, throttling, authentication · Hands-on lab: deploy a web application connected to RDS PostgreSQL |
AWS IAM: users, groups, roles, and least-privilege JSON policies · IAM best practices: MFA, access key rotation, and service roles · Encryption: server-side (SSE-S3, SSE-KMS) and client-side · AWS KMS: customer-managed keys and envelope encryption · Security groups vs NACLs: stateful vs stateless filtering · Threat detection: GuardDuty, AWS Config, CloudTrail, Security Hub · DDoS protection: AWS Shield Standard/Advanced and WAF rules · Hands-on lab: implement IAM roles, enable CloudTrail, configure WAF |
AWS Well-Architected Framework: all 6 pillars in depth · High availability: multi-AZ, auto-scaling, and fault tolerance · Disaster recovery: RTO, RPO, pilot light, warm standby, multi-site · Infrastructure as Code: AWS CloudFormation and Terraform basics · CI/CD on AWS: CodeCommit, CodeBuild, CodeDeploy, CodePipeline · Monitoring: CloudWatch metrics, alarms, dashboards, and X-Ray tracing · Cost management: Cost Explorer, Budgets, Trusted Advisor, Savings Plans · Azure and GCP overview: key equivalent services comparison · Hands-on lab: build a CI/CD pipeline using GitHub Actions deploying to AWS |
Design: architecture diagram for a three-tier web application on AWS · Build: frontend (S3 + CloudFront), backend (Lambda + API Gateway), database (RDS) · Security: IAM roles, security groups, HTTPS with ACM certificates, WAF · Monitoring: CloudWatch dashboards, alarms, and SNS notifications · Cost analysis: estimate monthly cost with the AWS Pricing Calculator · Documentation: write a technical architecture document with design rationale · Presentation: present architecture to a technical panel with live demo and Q&A |
Design and deploy cloud infrastructure on AWS with Azure/GCP equivalents understanding · Implement cloud security using IAM, KMS, and AWS security services · Build CI/CD pipelines and automate deployments with Infrastructure as Code · Optimise cloud costs using AWS Cost Explorer, Savings Plans, and right-sizing · Achieve a credential benchmarked against AWS CLF-C02, Google ACE, and Azure AZ-900 |
Complete all 6 modules, deploy a real three-tier web application on AWS, and pass a 65-question proctored examination (minimum 75%). All hands-on labs must be completed with screenshot evidence. |
Cloud Engineer, Solutions Architect (Junior), DevOps Engineer, Cloud Security Analyst, Platform Engineer, Site Reliability Engineer (SRE). Average starting salary: $75,000–$110,000 USD. |
CCA — Certified Cybersecurity Analyst (CCA)
★ Why this certification was added: There is a global shortage of 3.4 million cybersecurity professionals (ISC2, 2023). Cybersecurity is the fastest-growing technology career globally. Every organisation — government, NGO, business, hospital, school — needs cybersecurity expertise. Aligned to CompTIA Security+, CEH v12, and ISC2 CC — three of the world’s most widely held security credentials. |
“Defend everything. Fear nothing.” A comprehensive, internationally benchmarked cybersecurity certification covering network security, ethical hacking, web application security, cryptography, incident response, and security operations — aligned to CompTIA Security+, CEH v12, and ISC2 CC standards. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
|
Audience |
IT professionals, software developers, network engineers, system administrators, government workers, and students entering cybersecurity |
|
Standards |
CompTIA Security+ (SY0-701) · CEH v12 (Certified Ethical Hacker) · ISC2 Certified in Cybersecurity (CC) · NIST Cybersecurity Framework (CSF 2.0) · OWASP Top 10 (2021) · MITRE ATT&CK Framework |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored examination (minimum 75%) + penetration testing lab report + incident response simulation |
|
Certificate |
CCA Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
CIA Triad: confidentiality, integrity, and availability in practice · Security concepts: authentication, authorisation, non-repudiation, least privilege · Threat actors: nation-states, organised crime, hacktivists, and insiders · Attack taxonomy: cyber kill chain, MITRE ATT&CK framework, and diamond model · Malware types: viruses, worms, trojans, ransomware, spyware, rootkits, botnets · Phishing attacks: spear-phishing, vishing, smishing, and business email compromise · Security frameworks: NIST CSF 2.0, ISO 27001, CIS Controls v8, and COBIT 2019 · Legal and ethical landscape: CFAA, GDPR, CCPA, and Nigeria Cybercrime Act 2015 |
TCP/IP model: protocols, attack surfaces, and packet analysis with Wireshark · Network devices: NGFWs, IDS/IPS, proxies, load balancers, and honeypots · VPNs: site-to-site, remote access, SSL VPN, WireGuard, and IPsec · TLS/SSL: handshake, certificate validation, cipher suites, and TLS 1.3 · Wireless security: WPA3, EAP methods, and wireless attack detection · Network scanning: Nmap host discovery, port scanning, and OS fingerprinting · Network hardening: VLAN segmentation, DMZ design, and NAC · Hands-on lab: capture and analyse a network attack using Wireshark |
OS hardening: CIS benchmarks for Windows Server and Ubuntu Linux · Patch management: vulnerability lifecycle and automated patching strategies · Vulnerability scanning: Nessus and OpenVAS — interpreting CVSS scores · EDR concepts: CrowdStrike, SentinelOne, and Microsoft Defender for Endpoint · Malware analysis — static: PE headers, YARA rules, and VirusTotal · Malware analysis — dynamic: sandbox analysis with Cuckoo and Any.Run · Log analysis: Windows Event Logs, Linux syslog, and auditd · SIEM fundamentals: Splunk and Microsoft Sentinel — ingestion and alerting · Hands-on lab: vulnerability scan on a lab VM and write a remediation report |
Penetration testing methodology: recon, scanning, exploitation, reporting · Legal authorisation: Rules of Engagement and written permission requirements · OSINT: Shodan, Maltego, theHarvester, and Google dorking techniques · Exploitation with Metasploit: modules, payloads, encoders, post-exploitation · Password attacks: Hashcat, John the Ripper, and credential stuffing · Privilege escalation on Linux: SUID, sudo misconfigurations, cron jobs · Privilege escalation on Windows: token impersonation, AlwaysInstallElevated · Professional penetration test report writing with CVSS-scored findings · Hands-on lab: full black-box penetration test on a dedicated lab environment |
OWASP Top 10 (2021): all ten vulnerabilities — exploitation and remediation · SQL injection: manual testing, error-based, blind, time-based, and SQLmap · Cross-site scripting (XSS): reflected, stored, and DOM-based · CSRF, IDOR, and broken access control: testing and exploitation · Burp Suite: intercepting proxy, scanner, intruder, repeater, decoder · API security testing: REST and GraphQL authentication bypass · Secure code review: vulnerabilities in Python, JavaScript, and PHP · DevSecOps: integrating SAST (Semgrep, SonarQube) and DAST into CI/CD · Hands-on lab: exploit full OWASP Top 10 on a deliberately vulnerable web app |
Cryptographic foundations: AES, RSA, ECC, SHA-2, and bcrypt · PKI and X.509 certificates: CAs, chains, CSR, OCSP, and pinning · TLS deep dive: handshake, cipher suite negotiation, and TLS 1.3 · MFA: TOTP (RFC 6238), FIDO2/WebAuthn, push-based, and hardware keys · IAM: SSO, SAML 2.0, OAuth 2.0, OpenID Connect, and LDAP · Zero-trust architecture: BeyondCorp model and microsegmentation · Secrets management: HashiCorp Vault and AWS Secrets Manager · Hands-on lab: implement end-to-end encryption and configure OAuth 2.0 SSO |
SOC structure: tiers, roles, tools, daily operations, and shift handover · SIEM advanced: correlation rules, use case development, threat hunting · NIST SP 800-61 incident response lifecycle: all six phases in depth · Threat hunting: hypothesis-driven investigation and SIGMA rules · Digital forensics: chain of custody, memory forensics (Volatility), disk forensics (Autopsy) · Business continuity: RTO, RPO, backup strategies, and failover testing · Threat intelligence: IOC/TTP analysis, MISP, OpenCTI, and STIX/TAXII · Capstone: full penetration test with professional report — executive summary, CVSS findings, remediation roadmap |
Identify, exploit (ethically), and remediate vulnerabilities across networks, systems, and web apps · Conduct professional penetration tests and write client-ready reports with CVSS scores · Operate in a Security Operations Centre and respond to real cyber incidents · Implement cryptographic controls, PKI, and zero-trust architecture · Achieve a credential benchmarked against CompTIA Security+, CEH v12, and ISC2 CC |
Complete all 7 modules, pass a 75-question proctored examination (minimum 75%), submit a professional penetration test report with CVSS-scored findings, and complete an incident response simulation. |
Cybersecurity Analyst, Penetration Tester (Junior), SOC Analyst (L1/L2), Security Engineer, Incident Responder, Vulnerability Analyst, Bug Bounty Hunter. Average starting salary: $70,000–$100,000 USD. |
CAPE — Certified AI & Prompt Engineer (CAPE)
★ Why this certification was added: AI literacy is now a fundamental professional skill globally. The World Economic Forum lists AI as the single most important future-of-work skill. Prompt engineering is one of the most in-demand skills of 2024–2025. This certification is relevant from advanced secondary level upward and is aligned to OpenAI, Anthropic, Google DeepMind, and EU AI Act 2024 standards. |
“Master AI. Build the future.” A cutting-edge certification in AI fundamentals, LLM architecture, advanced prompt engineering, RAG pipelines, agentic AI systems, and responsible AI — covering both the theory and practice of building real AI-powered applications. |
| Programme Details | Information |
|---|---|
|
Level |
Advanced Secondary, University & Professional — all levels welcome |
|
Audience |
Developers, business professionals, researchers, educators, entrepreneurs, and any professional who works with or wants to build AI tools |
|
Standards |
OpenAI Developer Best Practices · Anthropic Constitutional AI Standards · Google AI Principles · DeepLearning.AI Curriculum · EU AI Act (2024) Compliance Framework · IEEE Ethics in Artificial Intelligence |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored examination (minimum 75%) + deployed AI-powered application + 1,000-word ethical AI reflection report |
|
Certificate |
CAPE Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
History of AI: from Turing and expert systems to deep learning and generative AI · Types of AI: narrow AI, generative AI, multimodal AI, and AGI — current state · Machine learning: supervised, unsupervised, and reinforcement learning fundamentals · Neural networks: architecture, activation functions, and backpropagation (conceptual) · Key milestones: AlexNet, AlphaGo, GPT-3, ChatGPT, GPT-4, Claude 3, Gemini, Llama 3 · AI ecosystem: OpenAI, Anthropic, Google DeepMind, Meta AI, Mistral AI · AI limitations: hallucinations, knowledge cutoffs, context windows, and bias |
How LLMs work: tokenisation, embeddings, transformers, and self-attention · Training process: pre-training, instruction fine-tuning, RLHF, and Constitutional AI · Context windows: GPT-4o (128k), Claude 3.5 (200k), Gemini 1.5 Pro (1M tokens) · Model families: GPT-4o, Claude 3.5, Gemini 1.5 Pro, Llama 3.1, Mistral, Phi-3 · Multimodal models: text, image, audio, video, and document inputs · Fine-tuning vs prompting vs RAG — cost, performance, and selection criteria · Open-source models: running Llama and Mistral locally with Ollama · Evaluating LLM outputs: BLEU, ROUGE, human evaluation, LLM-as-judge |
What is prompt engineering? The skill, craft, and science · Zero-shot prompting: direct instruction without examples · Few-shot prompting: providing examples to guide model behaviour · Chain-of-thought (CoT): step-by-step reasoning — standard and zero-shot CoT · Role and persona prompting: system message design and voice control · Output format control: JSON, markdown tables, XML, and structured data · Iterative prompt refinement: diagnosing failures and systematic A/B testing · Hands-on lab: 20 real prompt engineering challenges across writing, coding, and analysis |
Tree-of-thought (ToT): exploring multiple reasoning paths and self-evaluation · ReAct prompting: reasoning and acting — thought, action, observation loops · Self-consistency: sampling multiple chains and selecting majority answer · Prompt injection attacks: direct/indirect injection, jailbreaking, prompt leaking · Defensive prompting: input sanitisation, guardrails, robust system prompt design · Agentic AI: agents, tool use, function calling, and multi-step reasoning · Multi-agent systems: orchestration, routing, handoffs, and collaboration · LangGraph and CrewAI: building stateful multi-agent workflows · Hands-on lab: build a ReAct agent that uses tools to answer complex questions |
Retrieval-Augmented Generation (RAG): architecture, benefits, limitations · Embeddings: vector representations and semantic meaning · Vector databases: Pinecone, Weaviate, ChromaDB, Qdrant — similarity search · Building a RAG pipeline: chunking, embedding, indexing, retrieval, generation · Advanced RAG: query rewriting, HyDE, re-ranking, and hybrid search · LangChain and LlamaIndex: orchestrating LLM chains and document pipelines · OpenAI and Anthropic APIs: authentication, parameters, streaming, best practices · Deploying AI apps: Streamlit, Gradio, FastAPI, and Hugging Face Spaces · Hands-on lab: build a full RAG-powered document Q&A system with LangChain |
AI ethics frameworks: beneficence, non-maleficence, autonomy, and justice · Bias in AI: dataset bias, algorithmic bias, detection, and mitigation · Transparency: LIME, SHAP, and communicating AI decisions to stakeholders · AI safety: alignment, reward hacking, and Constitutional AI · Privacy in AI: data minimisation, differential privacy, and GDPR compliance · EU AI Act (2024): risk categories, prohibited uses, and high-risk obligations · Red-teaming: adversarial testing, failure mode discovery, responsible disclosure · Capstone: design, build, and present an AI-powered application for a real problem · Ethical reflection report: document risks, mitigations, and societal impact |
Master prompt engineering from zero-shot basics to advanced agentic AI workflows · Build production AI applications using LangChain, RAG, and the major AI APIs · Evaluate and select the right LLM for any business or research use case · Design responsible AI systems compliant with the EU AI Act and global ethical standards · Achieve a credential aligned to OpenAI, Anthropic, Google AI, and EU AI Act standards |
Complete all 6 modules, pass a 60-question proctored examination (minimum 75%), submit a deployed AI-powered application with GitHub documentation, and write a 1,000-word ethical AI reflection report. |
AI Engineer, Prompt Engineer, ML Engineer, AI Product Manager, AI Consultant, LLM Application Developer, AI Safety Researcher. Average starting salary: $80,000–$140,000 USD. |
CMAD — Certified Mobile App Developer (CMAD)
★ Why this certification was added: Mobile is the primary computing platform for over 6 billion people globally — and in Africa, mobile dominates internet access. Flutter (Google) and React Native (Meta) are used by thousands of companies worldwide. Mobile development is a core employability and entrepreneurship skill, especially across the African continent. |
“Build apps that billions use.” A professional mobile application development certification covering cross-platform development with Flutter (Dart) and React Native (JavaScript/TypeScript) — from UI design through state management, testing, performance optimisation, and publishing on Google Play and Apple App Store. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
|
Audience |
Software developers, web developers transitioning to mobile, entrepreneurs, CS graduates, and freelancers building mobile products |
|
Standards |
Google Flutter Certification Standards · Meta React Native Developer Standards · Apple Human Interface Guidelines (HIG) · Google Material Design 3 · Google Play Policy · App Store Review Guidelines |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored examination (minimum 75%) + published or submitted mobile application + code review session |
|
Certificate |
CMAD Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
Mobile ecosystem: iOS vs Android — market share, architecture, and developer tools · Native vs cross-platform: Flutter, React Native, Kotlin, Swift — trade-offs · Setting up environments: Android Studio, Xcode, VS Code, Dart/Flutter SDK · Mobile UI/UX principles: touch targets, gestures, and haptic feedback · Platform conventions: Material Design 3 and Apple Human Interface Guidelines · App architecture: MVC, MVVM, Clean Architecture, feature-first folder structure · App stores: Google Play Console and Apple App Store Connect — setup and policies · Performance: 60fps/120fps targets, jank, memory management, and battery usage |
Dart language: variables, types, null safety, functions, classes, mixins, extensions · Asynchronous Dart: Future, async/await, Stream, and StreamController · Flutter architecture: widget tree, element tree, render tree, and Flutter engine · StatelessWidget vs StatefulWidget: lifecycle methods and setState() · Core layout widgets: Container, Row, Column, Stack, Expanded, Flexible, Padding · Scaffold and MaterialApp: structure, ThemeData, and dark mode support · Hot reload vs hot restart: the Flutter developer feedback loop · Flutter Inspector and DevTools: debugging and performance profiling · Hands-on lab: build a Dart class hierarchy for a real domain model |
Material 3 widgets: AppBar, BottomNavigationBar, NavigationRail, Drawer, FAB · Custom widgets: building reusable components through composition · Lists and grids: ListView.builder, GridView.builder, CustomScrollView · Forms and validation: TextFormField, Form key, and custom validators · Animations: implicit (AnimatedContainer) and explicit (AnimationController, Tween) · Navigation with GoRouter: named routes, deep linking, and navigation guards · Responsive layouts: LayoutBuilder, MediaQuery, and OrientationBuilder · Custom painting: CustomPainter and Canvas API · Hands-on lab: multi-screen shopping app UI with navigation and animations |
State management: setState, Provider, Riverpod, BLoC, GetX — comparison · Riverpod: providers, notifiers, async providers, and code generation · BLoC pattern: events, states, Cubit vs Bloc, and testing in isolation · HTTP networking: Dio and http packages — GET, POST, interceptors, retry · JSON serialisation: dart:convert, json_serializable, and Freezed · REST API: authentication headers, error handling, loading/error/success states · Local storage: SharedPreferences, Hive, Isar, and SQLite with Drift ORM · Firebase: Authentication, Cloud Firestore, Storage, and Crashlytics · Hands-on lab: task management app with Riverpod and Firestore sync |
React Native architecture: bridge vs JSI new architecture (Fabric + TurboModules) · Setup: React Native CLI vs Expo — differences, trade-offs, and use cases · Core components: View, Text, Image, ScrollView, FlatList, TextInput · StyleSheet API: flexbox in React Native and responsive styling · Navigation with React Navigation v6: stack, tab, drawer, nested navigators · State management: React Query for server state and Zustand for client state · Native modules: Camera, Maps, Push Notifications, Biometrics, Payments · Expo: Expo Router (file-based routing), EAS Build, and OTA updates · Hands-on lab: port a Flutter app concept to React Native and compare implementations |
Unit testing: flutter_test, mockito, and mocktail · Widget testing: pumpWidget, finder APIs, and UI component behaviour · Integration testing: patrol package — E2E on real devices and emulators · Performance profiling: Flutter DevTools timeline — identifying jank · Optimisations: const constructors, RepaintBoundary, lazy loading, image caching · Mobile security: certificate pinning, flutter_secure_storage, root/jailbreak detection · Obfuscation: R8/ProGuard for Android, bitcode for iOS · Crash reporting: Firebase Crashlytics, Sentry, and Firebase Analytics · Hands-on lab: profile and optimise a slow Flutter app to consistent 60fps |
Product definition: user stories, acceptance criteria, and feature backlog · Design: Figma wireframes, component library, and design system · Architecture: state management, navigation, data layer decisions · Build: complete mobile app with auth, CRUD, and real-time data · Test: unit, widget, integration tests — minimum 70% code coverage · Performance: profile on Android and iOS — resolve all jank and memory issues · Security: secure storage, certificate pinning, OWASP Mobile Top 10 checklist · Publish: submit to Google Play Store (internal testing) or Apple TestFlight · Demo day: live demo, architecture walkthrough, Q&A with technical panel |
Build cross-platform mobile apps for Android and iOS using Flutter and React Native · Implement state management, API integration, local storage, and Firebase · Write comprehensive test suites and optimise mobile app performance to 60fps · Publish applications to Google Play Store and Apple App Store · Achieve a credential benchmarked against Google Flutter and Meta React Native standards |
Complete all 7 modules, publish or submit a complete mobile application to Google Play (internal testing) or Apple TestFlight, and pass a code review with an instructor (minimum 75%). |
Mobile Developer (Flutter/React Native), iOS Developer, Android Developer, Full-Stack Mobile Engineer, Mobile Product Engineer, Freelance App Developer. Average starting salary: $70,000–$110,000 USD. |
CHDA — Certified Healthcare Data Analyst (CHDA)
★ Why this certification was added: Healthcare is the world’s largest industry and one of the fastest growing sources of data. Hospitals, clinics, public health agencies, insurance companies, WHO, CDC, UNICEF, and ministries of health all urgently need professionals who can analyse health data, interpret clinical outcomes, track disease patterns, and support evidence-based health decisions. Healthcare data analytics requires specialist knowledge of health data standards (HL7, FHIR, ICD-10), clinical metrics, epidemiological methods, and health information systems — making it a distinct, high-value certification beyond general data analytics. |
“Save lives with data. The most meaningful analytics career there is.” A comprehensive, internationally benchmarked healthcare data analytics certification covering health data standards, electronic health records, clinical and epidemiological analysis, disease surveillance, health outcomes measurement, and health information systems — using Excel, SPSS, R, Python, Power BI, and SQL in healthcare contexts. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional |
|
Audience |
Public health professionals, clinical researchers, hospital administrators, health information officers, epidemiologists, medical students and graduates, NGO health programme staff, government health ministry officers, WHO/UNICEF/CDC staff |
|
Standards |
HL7 FHIR Standards · ICD-10/ICD-11 Classification · WHO Health Data Standards · HIPAA Data Privacy Framework · SNOMED CT · CDC Epidemiological Methods · Global Health Observatory (GHO) Standards · DHIS2 Health Information System |
|
Duration |
6 months |
|
Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
|
Assessment |
Proctored online examination (minimum 75%) + healthcare data analysis project with clinical dashboard, epidemiological report, and DHIS2 exercise |
|
Certificate |
CHDA Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
Healthcare data ecosystem: types of health data — clinical, administrative, financial, genomic · Key actors: hospitals, clinics, public health agencies, insurance, governments, WHO, CDC · Health information systems: DHIS2, OpenMRS, HMIS, and national health databases · Introduction to health data standards: HL7, FHIR, ICD-10/ICD-11, SNOMED CT, LOINC · Data quality in healthcare: completeness, accuracy, timeliness, consistency · Health data privacy and ethics: HIPAA, GDPR for health data, patient confidentiality · The analytics hierarchy: from raw EHR data to population health insights · Overview of tools used: Excel, SPSS, R, Python, Power BI, SQL, and DHIS2 |
Electronic Health Records (EHR): structure, fields, and extracting data for analysis · Hospital Information Systems (HIS): inpatient, outpatient, emergency, and lab data · Vital registration systems: birth and death registration, cause-of-death coding with ICD-10 · Survey data in health: DHS (Demographic and Health Survey), MICS, and LSMS health modules · Disease surveillance systems: notifiable disease reporting, sentinel surveillance, IDSR · DHIS2: navigating the platform, extracting data, and building indicators · Routine Health Information Systems (RHIS): facility-level data quality and completeness · Claims and billing data: ICD codes in insurance and administrative datasets · Hands-on lab: extract and clean a real DHS health dataset for analysis |
Core epidemiological measures: incidence, prevalence, mortality rate, case fatality rate · Attack rate, secondary attack rate, and outbreak investigation methods · Crude rates vs age-standardised rates: why standardisation matters for comparisons · Measures of association: relative risk (RR), odds ratio (OR), and attributable risk · Study designs: cross-sectional, cohort, case-control — strengths and limitations for analysis · Life tables and survival analysis: Kaplan-Meier curves and log-rank test · Outbreak investigation: steps, spot maps, and epidemic curves · Disease burden metrics: DALY (Disability-Adjusted Life Year), QALY, YLL, and YLD · Calculating and interpreting epidemiological measures in Excel and R · Hands-on lab: conduct a complete outbreak investigation analysis on a simulated dataset |
Understanding clinical trial data: phases, endpoints, and outcome variables · Patient demographics analysis: age, sex, comorbidities, and risk stratification · Clinical outcome measures: mortality, readmission rates, length of stay, complications · Laboratory data analysis: reference ranges, abnormal values, and clinical interpretation · Vital signs trends: blood pressure, temperature, pulse, oxygen saturation — time series · Survival analysis in clinical data: Kaplan-Meier and Cox proportional hazards in R · Risk adjustment: controlling for patient case mix in hospital performance comparison · Diagnostic test evaluation: sensitivity, specificity, PPV, NPV, ROC curves, and AUC · Drug safety analysis: adverse event reporting and pharmacovigilance basics · Hands-on lab: analyse a clinical dataset — patient outcomes, readmission predictors, and survival |
Principles of health data visualisation: choosing the right chart for clinical and public health data · Disease mapping: choropleth maps, dot maps, and spatial clustering in R and Power BI · Epidemic curves: constructing and interpreting epi curves by date of onset · Health dashboards in Power BI: KPIs, trend lines, and drill-through for health managers · DHIS2 dashboards: building real-time health facility dashboards from HMIS data · Visualising mortality and survival: Kaplan-Meier plots, funnel plots for hospital comparison · Demographic pyramid charts: population age-sex structure and disease burden · Health scorecards: balanced scorecard frameworks for health system performance · Communicating health data to non-technical audiences: ministers, donors, and communities · Hands-on lab: build a complete public health surveillance dashboard in Power BI |
Health information system architecture: data collection, transmission, storage, and use · DHIS2 in depth: metadata, organisation units, data elements, indicators, and validation rules · OpenMRS: open-source EHR for low-resource settings — data extraction and analysis · Interoperability: HL7 FHIR APIs for connecting health systems and extracting data · Mobile health (mHealth): ODK and KoboToolbox for community health worker data collection · Telemedicine data: patient records, consultation logs, and outcome tracking · AI and machine learning in healthcare: clinical decision support, diagnosis prediction, risk scoring · Digital health ethics: algorithmic bias in clinical tools and equitable AI in health · Introduction to genomic data: basics of variant analysis and precision medicine data · Hands-on lab: connect to a FHIR API sandbox and extract patient data for analysis |
Burden of disease analysis: global, regional, and national estimates from GBD, WHO GHO · Health equity analysis: disaggregating health data by sex, age, income, geography, and ethnicity · Programme evaluation in health: measuring impact of vaccination, nutrition, and malaria programmes · Health system performance metrics: access, quality, efficiency, equity, and sustainability (WHO framework) · Writing a public health analytical report: structure, evidence, limitations, and recommendations · Policy briefs for health ministries and donors: WHO, USAID, Global Fund formats · Capstone project: choose a health dataset (DHS, DHIS2, or clinical) — conduct a full analysis including cleaning, epidemiological measures, visualisation, and a written public health report |
What is DHIS2? Architecture, global adoption across 73+ countries, and Nigeria NHMIS context · DHIS2 interface: navigating apps — data entry, analytics, maps, dashboards, and event capture · Organisation unit hierarchy: national, state, LGA, facility — understanding the DHIS2 structure · Data elements, indicators, data sets, and periods: the building blocks of DHIS2 data · Data entry in DHIS2: aggregate data entry, validation rules, completeness, and timeliness monitoring · Importing data into DHIS2: Excel, CSV, XML, and JSON/ADX data exchange formats · DHIS2 analytics app: pivot tables, charts, and dashboards for health managers · DHIS2 data visualiser: bar, line, gauge, and combined charts for facility and district data · DHIS2 maps app: thematic mapping of health indicators — choropleth and bubble maps by district · Data quality tools in DHIS2: completeness/timeliness dashboard, min-max outlier detection, and consistency checks · DHIS2 Tracker: individual-level data — antenatal care, immunisation, and case-based surveillance · DHIS2 Android Capture app: offline mobile data collection for community health workers · DHIS2 Web API: extracting data in JSON/CSV for integration with Power BI, R, Python, and Excel · DHIS2 in Nigeria and Africa: NHMIS, state HMIS configurations, and real-world implementation challenges · Hands-on lab: build a complete district health dashboard in DHIS2 — import data, create indicators, build visualisations, and share with a management audience |
Analyse clinical, epidemiological, and population health datasets using industry-standard tools · Apply health data standards (ICD-10, FHIR, HL7, SNOMED CT) in real healthcare analytics projects · Build disease surveillance dashboards and conduct outbreak investigation analysis · Use survival analysis, risk adjustment, and diagnostic test evaluation in clinical data · Build professional DHIS2 dashboards and extract health data via the FHIR API · Produce public health reports and dashboards for health ministries, NGOs, and donors · Achieve a credential aligned to WHO, CDC, HIPAA, DHIS2, and HL7 FHIR standards |
Complete all 8 modules, pass a proctored examination (minimum 75%), submit a healthcare data analysis project including a disease surveillance dashboard, clinical outcome analysis, a DHIS2 dashboard, and a 1,500-word public health report. |
Healthcare Data Analyst, Health Information Officer, Epidemiologist (Junior), Public Health Analyst, Clinical Data Manager, Health Intelligence Officer, Hospital Performance Analyst, WHO/UNICEF/CDC Data Analyst. Average starting salary: $55,000–$95,000 USD. |
CBDA — Certified Business Data Analyst (CBDA)
★ Why this certification was added: Every business — from a small Lagos shop to a multinational corporation — now needs professionals who can translate data into commercial decisions. Business data analytics is the most broadly employable form of data skills, cutting across finance, marketing, operations, HR, and strategy. It uses business intelligence tools (Power BI, Tableau, Excel), financial analysis, KPI frameworks, customer analytics, and business dashboards — distinct from pure data science or academic analytics. This is the certification for people who want to advance in business careers. |
“Turn business data into competitive advantage.” A practical, business-focused data analytics certification covering financial data analysis, marketing and customer analytics, operations and HR analytics, KPI framework design, business intelligence dashboards, and data-driven decision making — using Excel (advanced), Power BI, Tableau, SQL, and Python for business applications. |
| Programme Details | Information |
|---|---|
|
Level |
University & Professional — suitable for anyone in a business role |
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Audience |
Business managers, marketing professionals, finance officers, HR professionals, operations analysts, entrepreneurs, MBA students, management consultants, and any professional making business decisions with data |
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Standards |
Microsoft Power BI Data Analyst Associate (PL-300) · Tableau Desktop Certified Associate · Google Analytics 4 · CFA Institute Data Analytics Standards · ACCA Business Analytics · Harvard Business Analytics Programme Standards · DAMA International DMBOK |
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Duration |
6 months |
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Format |
Self-paced · Live instructor-led · Cohort-based · Blended |
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Assessment |
Proctored online examination (minimum 75%) + business analytics project with executive dashboard, EViews econometric output, and strategic recommendations report |
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Certificate |
CBDA Certificate — Ukeh-Adah Alliance Services Ltd |
Course modules
What is business analytics? Descriptive, diagnostic, predictive, and prescriptive analytics · The data-driven organisation: how leading companies use data to outcompete · Business analytics roles: data analyst, BI developer, business intelligence manager · KPI (Key Performance Indicator) frameworks: designing, selecting, and tracking business KPIs · Balanced Scorecard: strategy maps, perspectives, and lagging vs leading indicators · Data governance in business: data ownership, quality standards, and master data management · DAMA DMBOK framework: introduction to enterprise data management · Introduction to business analytics tools: Excel, Power BI, Tableau, SQL, Python, Google Analytics |
Financial statements: income statement, balance sheet, and cash flow statement — structure and relationships · Ratio analysis: liquidity, profitability, solvency, and efficiency ratios — calculation and interpretation · Trend analysis: year-on-year and month-on-month growth, moving averages, and forecasting · Budget vs actual analysis: variance analysis, waterfall charts, and management reporting · Revenue analysis: product-level, segment-level, and geographic revenue breakdown · Cost analysis: fixed vs variable costs, cost allocation, and contribution margin analysis · Financial modelling in Excel: building a 3-statement financial model and sensitivity analysis · Cash flow forecasting: 13-week cash flow model and working capital analysis · Financial dashboards in Power BI: P&L dashboard, revenue tracker, and budget variance views · Hands-on lab: analyse a company’s 3-year financial statements and build a management dashboard |
Customer segmentation: RFM analysis (Recency, Frequency, Monetary value) in Excel and Python · Customer Lifetime Value (CLV): calculation methods and strategic implications · Marketing funnel analysis: acquisition, activation, retention, referral, and revenue (AARRR) · Campaign performance analysis: CTR, conversion rate, ROI, and cost per acquisition (CPA) · A/B testing for marketing: statistical significance, test design, and interpreting results · Web analytics with Google Analytics 4: sessions, users, conversion events, and attribution · Social media analytics: engagement rate, reach, impressions, and sentiment basics · Churn analysis: customer retention analysis and churn prediction using logistic regression · Market basket analysis: association rules, support, confidence, and lift — product recommendations · Hands-on lab: conduct a full customer segmentation and marketing performance analysis |
Operational KPIs: OEE (Overall Equipment Effectiveness), cycle time, throughput, and defect rates · Inventory analysis: ABC analysis, EOQ (Economic Order Quantity), and stockout risk · Supply chain analytics: lead time analysis, supplier performance, and on-time delivery tracking · Process improvement: Lean and Six Sigma data tools — control charts, Pareto, and fishbone · Demand forecasting: moving averages, exponential smoothing, and seasonal decomposition in Excel and R · Logistics and delivery analytics: route efficiency, delivery SLA compliance, and last-mile analytics · Project analytics: Gantt charts, critical path, earned value analysis (EVM) · HR analytics: headcount, turnover rate, time-to-hire, performance distribution, and payroll analysis · Operations dashboards in Power BI: production, supply chain, and workforce KPI views · Hands-on lab: build an operations analytics report on a manufacturing or logistics dataset |
Power BI advanced: composite models, incremental refresh, row-level security (RLS) · Advanced DAX: time intelligence, running totals, dynamic segmentation, and SWITCH statements · Tableau advanced: LOD expressions, table calculations, blending data sources, and Tableau Prep · Business storytelling with data: structuring a data story — situation, complication, resolution · Report design principles: layout, hierarchy, white space, and mobile-first dashboard design · Self-service BI: empowering business users to explore data without analyst support · Real-time dashboards: streaming data, automatic refresh, and live connections in Power BI · Automated reporting: Power Automate for scheduled report distribution · Executive presentations: translating analytics into board-level insights and strategic recommendations · Hands-on lab: build an advanced executive dashboard in Power BI with RLS and automated delivery |
Python for business: pandas for financial data, time series analysis, and business metrics · Business data cleaning in Python: handling real-world messy commercial datasets · Automated reporting with Python: generating Excel reports and PDF summaries programmatically · SQL for business: extracting revenue, customer, and operational data from relational databases · Advanced SQL for business: window functions for running totals, rankings, and cohort analysis · Python visualisation for business: matplotlib and plotly for commercial chart types · Predictive analytics for business: linear regression for sales forecasting in Python · Customer churn prediction: logistic regression model — build, evaluate, and deploy in Streamlit · Introduction to machine learning for business: decision trees for customer classification · Hands-on lab: build an end-to-end business analytics pipeline in Python — from data to automated report |
Why STATA and EViews matter for business economists, policy analysts, and development finance · STATA for business data: importing financial, market, and firm-level datasets · Regression analysis in STATA: OLS for sales, pricing, and demand modelling · Panel data in STATA: fixed effects and random effects for firm-level longitudinal business data · Difference-in-differences in STATA: evaluating the impact of business policy changes · Introduction to EViews: interface, workfile structure, and business time-series data · Unit root tests in EViews: ADF and KPSS stationarity tests for economic time series · OLS regression in EViews: estimation, diagnostic tests (heteroscedasticity, autocorrelation) · ARIMA modelling in EViews: Box-Jenkins methodology for business forecasting · VAR model in EViews: modelling interactions between macroeconomic and business variables · Granger causality test: testing lead-lag relationships between business and economic variables · GARCH model in EViews: volatility modelling for stock prices, exchange rates, and commodity prices · Forecasting in EViews: producing and evaluating business and economic forecasts · Hands-on lab: complete a business economic analysis in EViews — stationarity, regression, ARIMA, and forecast for a financial or commodity dataset |
Africa’s business data landscape: unique opportunities, challenges, and key data sources · Mobile money and fintech analytics: M-Pesa, Flutterwave, OPay, and mobile payment transaction data · Informal sector analytics: measuring and modelling informal market activity in Nigeria and Africa · SME financial analysis in emerging markets: micro-enterprise income, expenditure, and profitability · Agricultural value chain business analytics: farm-to-market price and trader margin calculation · Telecoms and digital economy: subscriber data, ARPU, churn, and data consumption trends · Energy analytics in Africa: electricity access, load shedding impact, and off-grid solar market data · Poverty and consumer analytics: living standards, expenditure patterns, and market segmentation · Cross-border trade: ECOWAS trade flows, import/export data, and informal commerce · Nigeria-specific data: NBS (National Bureau of Statistics), CBN, NIMC, and open government datasets · Donor and development finance analytics: tracking ODA flows, DFI investment, and development impact · Hands-on lab: analyse a Nigerian or African business dataset — mobile money transaction analysis or SME profitability study |
Competitive intelligence: using data to understand market position and competitor performance · Scenario analysis and sensitivity modelling: what-if analysis for business decisions · Data-driven strategy: translating analytics findings into strategic recommendations · Building a data culture: how to present analytics to sceptical executives and non-data leaders · Ethics in business analytics: data privacy, GDPR compliance, and responsible use of customer data · Consulting frameworks for data analysis: MECE, issue trees, and hypothesis-driven analysis · Capstone project: choose a real business dataset (sales, finance, marketing, or HR) — conduct a full analysis, build an executive dashboard, and present strategic recommendations in a written report |
Conduct financial, marketing, operations, and HR analytics using industry-standard business tools · Build executive-level BI dashboards in Power BI and Tableau with advanced DAX and LOD expressions · Apply Python and SQL to automate business reporting and build predictive commercial models · Conduct regression, panel data, and time-series analysis using STATA and EViews · Apply business analytics frameworks to African and Nigerian market contexts including mobile money and SME data · Design KPI frameworks and balanced scorecards aligned to business strategy · Communicate data findings to executive and board-level audiences with impact · Achieve a credential aligned to Microsoft PL-300, Tableau, Google Analytics, EViews, STATA, and DAMA DMBOK standards |
Complete all 9 modules, pass a proctored examination (minimum 75%), and submit a business analytics project including a cleaned business dataset, an executive Power BI dashboard, financial analysis, and a 1,500-word strategic recommendations report. |
Business Analyst, Data Analyst (Commercial), Marketing Analyst, Financial Analyst (Junior), Operations Analyst, BI Developer, Strategy Analyst, Management Consultant (Junior), Product Analyst. Average starting salary: $50,000–$90,000 USD. |
CODING & DATA ANALYTICS — YOUR TOOLS, YOUR WAY: PERSONALISED TOOL TRAINING PROGRAMME
Not seeing your tool? Tell us what you use — we will teach it.
At Ukeh-Adah Alliance Services Ltd, we believe that training should serve you — not force you to change the tools your work, your university, your employer, or your research demands. Our standard certifications cover the world’s most widely used tools. But we know that every client is different. Your organisation may use SAS. Your university department may require Matlab. Your company may run on Salesforce. Your research team may work in Stata SE with custom ado-files. Whatever tools you use or need — we will train you in them.
Our standard tools — already covered in our certifications Statistics & Data: IBM SPSS · STATA · EViews · R & RStudio · Python · NVivo · MATLAB (basic) Data Analytics: Microsoft Excel (Advanced) · Google Sheets · SQL · Power BI · Tableau · Looker Studio · Power Query Coding & Development: Python · JavaScript · React · Node.js · Flutter · Dart · React Native Cloud & Infrastructure: AWS · Microsoft Azure · Google Cloud Platform (GCP) · Docker · Kubernetes Cybersecurity: Metasploit · Nmap · Wireshark · Burp Suite · Nessus · Splunk AI & Machine Learning: TensorFlow · PyTorch · Scikit-learn · LangChain · Hugging Face · OpenAI API · Anthropic API GIS & Remote Sensing: QGIS · ArcGIS Online · Google Earth Engine · DHIS2 · ODK · KoboToolbox |
Bring your own tool — we train you in it
If you are a student, researcher, professional, or organisation that uses a specific tool not listed above — or you want focused, intensive training on just ONE tool from the list — simply tell us. Here are examples of tools clients have already brought to us for personalised training
Statistics & Research
SAS · SPSS AMOS · Mplus · HLM (hierarchical linear modelling) · Lisrel · SmartPLS · ATLAS.ti · Dedoose · MaxQDA · PASS (power analysis) · G*Power · Minitab · JMP · Gretl |
Business & Analytics
SAP Analytics · Salesforce CRM Analytics · Microsoft Fabric · Qlik Sense · Domo · Looker (LookML) · IBM Cognos · MicroStrategy · Oracle Analytics · Alteryx · KNIME · RapidMiner |
Development & Cloud
MATLAB · Julia · Scala · Go · Rust · Angular · Vue.js · Django · FastAPI · Spring Boot · Azure DevOps · Terraform · Ansible · Jenkins · Kubernetes advanced |
Specialist Tools
DHIS2 advanced · RedCAP · OpenMRS · LIMS (Laboratory Information Systems) · Kobo advanced · Survey Solutions (World Bank) · CSPro · EpiInfo · WinBUGS · JAGS (Bayesian) |
Don't see your tool? That is exactly the point.
If your tool is not listed anywhere above — that is fine. We do not need to have taught it before. Our instructors are experienced technologists and researchers who can assess any tool, prepare a custom training plan, and deliver focused sessions tailored to your specific needs and level. Submit your tool request and we will respond within 24 hours.
How the Personalised Tool Training works
Submit your tool request through our contact form, WhatsApp, or student portal — just name the tool and describe what you need to do with it |
Are you a complete beginner with this tool, intermediate, or do you need advanced/specialist features? Tell us your starting point |
What do you need to use this tool for? PhD analysis? Work project? Company training? Research publication? The more you tell us, the more targeted your training will be |
Our team reviews your request within 24 hours and prepares a personalised training curriculum for your specific tool, level, and purpose |
You receive focused, practical, one-on-one or small group training sessions on exactly the tool features and workflows you need — nothing more, nothing less |
Every personalised tool training engagement ends with a practical output — an analysis, a dashboard, a script, a model, or a report — using YOUR tool on YOUR data |
Upon completing your personalised training engagement, you receive a Ukeh-Adah Alliance Services Ltd Certificate of Training for the specific tool |
English (primary) · Local language support can be arranged on request
Our promise to every child and every family
One-on-one live sessions via Zoom or Google Meet · Small group sessions (2–5 people) · Corporate group training (6+ people) |
3 sessions minimum for any tool (we do not do one-off sessions — we ensure you actually learn) |
90 minutes per session — focused, practical, no wasted time |
Personalised training plan delivered within 24 hours of your request |
Bring your own dataset, your own project, your own real-world task — we train you on what you actually need to do |
Available for companies and institutions wanting to upskill entire teams on specific tools — custom pricing and delivery |
Certificate of Tool Training issued on completion — specific tool named, hours completed, skills covered |
“Your tool. Your data. Your timeline. Your way. That is the Ukeh-Adah promise.”
“Enrol Now — Join Thousands of Students and Researchers Worldwide”
“Get Certified. Build Skills. Change Your Future.”
IITA-CGIAR Research Fellow · CAC Registered · Over 15 Years of Excellence · Globally Recognised Certificates