Capability
You gain the ability to solve problems that people and organisations need solved.
A Xellux Technologies programme connecting you with tech excellence from June to December 2026.
More processes depend on software and connected tools.
Information has greater value when people can interpret it.
People can build solutions that serve others at scale.
Learning gives you capability. Applying it produces work others can see. That work can build credibility, open access to valuable networks and give you the ability to create systems that generate value beyond your own hours.
Each stage can expand your economic possibilities:
You gain the ability to solve problems that people and organisations need solved.
Projects turn your ability into visible evidence of what you can deliver.
When people can examine your work, they have a stronger reason to trust you with responsibility, opportunities and resources.
Credibility can bring you into the rooms where collaborators, employers, clients and new opportunities meet.
Skills and relationships can help you build processes, products or organisations that create value beyond the hours you personally work.
When you help build lasting systems, you can seek a stake in the value they continue to create.
The goal is bigger than learning a tool. It is building the capability, proof and credibility to access opportunity, create lasting value and participate in what you help build.
The ICT landscape is constantly evolving, with new tools and technologies emerging regularly.
Our training program stays up-to-date with the latest industry trends and equips participants with proficiency in relevant tools and technologies. From programming languages and development frameworks to data analysis tools and Business Analytics software, participants learn to leverage cutting-edge tools to solve complex problems efficiently.
Our online training services cover both self-paced and virtual live training. This is available for individuals, corporate organizations and groups. We have a flexible training schedule for your training needs any day, anytime, anywhere.
Also, our Hybrid training is designed to help you achieve your goals and take your skills to the next level.
With a unique blend of In-person and Online Learning, we'll give you all the benefits of modern classroom-based training, combined with the convenience and flexibility of online learning.
Swipe left In Mobile Device
Corporate Data Analytics Training Upskill or reskill your workforce with hands-on Data Analytics corporate training skills. Conduct the training onsite at your location or live online from anywhere.
More so, our Corporate Data Science training– a data-driven course designed to help modern enterprises to effectively focus on business values regardless of industry.
This course provides hands-on and project-based training on how to monitor and analyze business retail activities to boost sales.
Level up your team and chart the course to data transformation.
Our Physical training is beginner-friendly, practical and project-based .It is designed to help participants gain mastery of different technologies & tools that are used to analyze data and develop software in organizations. It is tailored to help gain real-world experience that can get you a job and grow your career.
We provides hands-on learning experiences where participants directly interact with instructors, software, and datasets. This hands-on approach allows learners to gain practical skills and immediately apply them to real-world scenarios. We offer personalized guidance tailored to individual learning styles and pace, enhancing comprehension and retention.
At Xellux Technologies, we encourage individuals to immerse themselves in project-based learning to unleash their full potential & thrive in the ever-evolving tech industry
In today's data-driven world,the ability to extract insights from vast amounts of data is invaluable.
Skill in data Science provide invaluable hands-on experience & highlight one's ability to tackle real-world challenges using cutting-edge technologies like TensorFlow, Scikit-learn & Seaborn
A core understanding of both front-end & back-end development learners can demonstrate their proficiency in designing,implementing & deploying complex applications.
Explore practical learning paths, projects and the work each course prepares you to undertake.
Learn to import, clean, analyse and present business data using Microsoft Excel. Develop your skills through practical exercises and two dashboard projects based on sales and product performance.
Beginners, graduates, career changers and professionals who need to analyse sales, customer, operational or financial data.
Beginner. No prior data analysis experience is required. The course progresses to practical intermediate Excel work.
Practical work: Organise an unstructured transaction worksheet into an Excel Table and identify sales records by date, product and region.
Practical work: Combine monthly sales files, remove duplicates, correct inconsistent product names and prepare a clean dataset for analysis. Add a new month's file and refresh the result.
Practical work: Calculate transaction values, monthly sales, customer counts and target variances. Build a report that updates when the underlying records change.
Practical work: Connect a sales transaction table to product and pricing tables. Retrieve categories and prices, then identify products that could not be matched.
Practical work: Investigate why overall revenue increased while one region declined. Rank products by sales and identify the records behind the regional change.
Practical work: Build an interactive report that allows a manager to compare sales by month, product category and region.
Practical work: Build a sales dashboard that shows revenue, growth, product performance and regional results.
Analyse an Adidas sales dataset and build an interactive Excel dashboard using PivotTables, PivotCharts and slicers.
Project output: An interactive Excel Slicer Dashboard and a short explanation of the findings.
Practical work: Present a short performance review explaining what changed, where it changed, what the evidence shows and what the business should examine next.
Use sales and product data to investigate business performance and present the results in an Excel dashboard.
Project output: A Sales & Product Dashboard and a short presentation explaining the findings and recommendations.
Excel-based reporting and entry-level analytical work in sales, operations, administration, customer services, finance and business support teams across industries.
This course develops practical Excel analysis capability. Roles requiring SQL, Power BI, programming or specialist industry knowledge may require further training or experience.
Learn to retrieve, investigate and manage data in relational databases. Write SQL queries to answer business questions, connect related tables and build a database that supports a working application.
Beginners, aspiring analysts, software developers, graduates and professionals who need to work with structured business data.
Beginner. The course starts with database fundamentals and progresses to multi-table analysis and practical database design.
Practical work: Explore a business database containing customers, products and orders. Identify how its tables relate and retrieve your first records.
Practical work: Find orders within a date range, identify customers in selected locations and inspect products that meet specific price and category conditions.
Practical work: Produce monthly sales totals, average order values and product rankings. Investigate differences between a record count and a distinct customer count.
Practical work: Connect customers, orders, order items and products to calculate revenue by product and customer. Find orders with missing related records.
Practical work: Identify customers who purchased from multiple categories, customers who have never ordered and products appearing in more than one sales period.
Practical work: Build a monthly performance report, rank products within each category and identify customers whose purchasing activity has changed.
Practical work: Create product and customer tables, add sample records, update incorrect entries and remove test records in a practice database.
Practical work: Improve a database design with duplicated customer information. Add appropriate relationships and test queries against the revised tables.
Design and implement a relational database for users, posts, comments and interactions.
Project output: A working database schema and SQL queries that demonstrate how the application retrieves its data.
Design a school database that can organise students, classes, enrolments and results.
Project output: A working school management database with reporting queries and demonstrated CRUD operations.
Entry-level SQL work in data analysis, reporting, business intelligence support, software development and database support across industries.
This course develops practical SQL querying and relational database foundations. Specialist database administration, advanced data engineering and senior analytical roles require further training and experience.
Connect business data, build a reliable data model, calculate meaningful measures and create interactive reports that help people understand performance and make decisions.
Aspiring BI analysts, reporting analysts, graduates, career changers and professionals who prepare business or management reports.
Beginner. Basic spreadsheet familiarity is useful, but prior Power BI experience is not required.
Practical work: Open a business dataset in Power BI Desktop, inspect its tables and define the questions a sales report should answer.
Practical work: Combine monthly sales files, correct inconsistent product categories, connect a product table and refresh the report when a new file is supplied.
Practical work: Connect sales, products, customers and dates in a model that supports accurate filtering and reporting.
Practical work: Create measures for revenue, orders, customers and average order value. Compare each result with the underlying records.
Practical work: Rank products by sales, calculate each region's share of revenue and compare performance across periods.
Practical work: Build an interactive report that allows a manager to examine sales by product, location and reporting period.
Practical work: Review a report containing an incorrect total and an unclear visual. Correct both, then present findings supported by the verified report.
Practical work: Prepare a report for a team, test the refresh process and explain how authorised colleagues would access it.
Build a Coca-Cola-themed business intelligence dashboard using a supplied training dataset. The project follows the reporting process from raw records to management findings.
Project output: A Power BI dashboard and a short presentation explaining the results. The training dataset is used for learning and is not represented as Coca-Cola's internal data.
Entry-level business intelligence, reporting and analytical support roles in sales, operations, finance, customer services and other data-driven teams.
This course builds practical Power BI reporting capability. Roles that require advanced data engineering, database administration or specialist domain experience may require additional training and experience.
Use Microsoft Excel to organise accounting records, prepare schedules, investigate balances and support financial reporting. Practise the work through journals, ledgers, reconciliations and statements.
Accounting students, accounts assistants, junior accountants, bookkeepers, auditors and finance staff who want practical Excel capability.
Beginner. The course starts with Excel fundamentals and progresses to accounting schedules and entry-level reporting work.
Practical work: Turn an untidy transaction list into a usable accounting register and identify overdue customer balances.
Practical work: Match transaction records to a chart of accounts, calculate totals by account and period, and flag missing account codes.
Practical work: Record a month of sales, purchases, receipts and payments; post them to the ledger and prepare a trial balance.
Practical work: Create a quotation, sales invoice and customer account record, then update the balance when a payment is received.
Project output: A debt schedule and receivables ageing report that a finance team can use for credit control.
Practical work: Produce a monthly expense analysis and a customer ageing report that update when new records are added.
Practical work: Reconcile an account statement to the ledger, investigate differences and correct errors in a working paper.
Practical work: Use a trial balance to prepare monthly statements and explain the largest movements in income and expenses.
Project output: A connected accounting workbook containing journal entries, a ledger, trial balance and financial statements.
Practical work: Build a loan repayment schedule and an asset depreciation schedule, then show how the results affect monthly reporting.
Project output: A loan repayment schedule and supporting cash flow statement.
Practical work: Compare actual expenses with a budget and test how changes in sales or costs would affect the result.
Work through a month of realistic transactions for a business and prepare the records an entry-level finance team would need to review.
Portfolio output: A completed accounting workbook and a short month-end review explaining the results.
Practical Excel work in accounts assistant, junior accounting, bookkeeping, audit support, credit control and finance operations teams across industries.
This course develops spreadsheet and applied accounting capability. Professional accounting qualifications, local reporting requirements and role-specific experience may also be required.
Build financial models that connect historical results to forecasts, cash flow, funding decisions and valuation. Learn to test assumptions, explain outcomes and present analysis that another finance professional can review.
Finance and accounting graduates, junior financial analysts, FP&A trainees, auditors and professionals who work with financial statements or budgets.
Basic Excel and financial statement knowledge are recommended. The course progresses from modelling foundations to applied forecasting and valuation.
Practical work: Repair a financial workbook with broken references and inconsistent assumptions, then build a clear input and output structure.
Project output: A dynamic budget tracker with a clear monthly summary and forecast.
Practical work: Retrieve historical results from a source table, calculate financial measures and compare two investments with differently timed cash flows.
Practical work: Build linked statements from supplied historical figures and explain the largest changes in profitability and working capital.
Project output: A linked balance sheet with supporting account schedules.
Practical work: Forecast a company's next reporting periods and investigate why the balance sheet fails to balance under one assumption.
Project output: A startup forecast model showing performance and funding needs.
Practical work: Test how lower sales, higher costs or slower collections affect profit and cash. Produce an Automated Budget Tracker and a Sensitivity and Scenario Analysis workbook.
x Practical work: Build a debt schedule and assess whether a business can meet its obligations under a lower-revenue scenario.
Project output: An investment appraisal workbook and a recommendation supported by its assumptions.
Practical work: Build a discounted cash flow valuation and compare it with results from comparable companies.
Project output: A comparable companies valuation with a clear range and supporting assumptions.
Practical work: Import a new reporting period, refresh the analysis, identify an incorrect source value and explain its effect on the forecast.
Practical work: Build the existing Xellux Stock Portfolio Tracker and prepare a Client Financial Analysis showing returns, exposures and questions for review.
Practical work: Produce the existing Xellux Dynamic Sales Dashboard and organise selected work into a Data Analysis Projects Portfolio.
A business provides historical financial statements, operating information and proposed growth plans. Assess its performance and build a forecast for management review.
Portfolio output: A financial model and a short presentation explaining the forecast and the decisions it informs.
Financial analysis and modelling work in FP&A, corporate finance, investment research, credit, consulting, audit support and finance teams.
The course develops modelling and analytical capability. Entry requirements vary by role; valuation, investment and senior finance positions may require relevant qualifications and professional experience.
Learn Python by building programs that solve everyday work problems. Progress from the language foundations to files, APIs, databases and tested automation that another person can run and understand.
Beginners, graduates, career changers and professionals who want to write software or automate repetitive tasks with Python.
Beginner. No programming experience is required. Learners progress to practical junior-level Python development tasks.
Practical work: Build a small invoice calculator that receives quantities and prices, calculates totals and handles invalid input.
Practical work: Process a list of customer orders, calculate each total and identify records that require review.
Practical work: Refactor the order-processing program into functions that calculate totals, validate records and produce a summary.
Project output: A working terminal-based Python program that manages orders and inventory.
Practical work: Read a folder of transaction files, identify incomplete records and export a clean CSV report.
Practical work: Automate a recurring weekly report from several input files and produce a log showing which files were processed or rejected.
Project output: A reusable Python reporting script with sample input, output and run instructions.
Practical work: Represent customers, products and orders with classes, then use them to calculate an order total and update stock.
Practical work: Retrieve information from an API and store selected records in a database for later reporting.
Practical work: Improve an existing automation script so it handles faulty inputs, produces clear errors and passes tests for its key calculations.
A small business receives orders in files and needs a reliable way to process them, track inventory and prepare a daily summary.
Portfolio output: A working Python application, sample data, tests and a short demonstration of its workflow.
Entry-level Python development, software support, business process automation, technical operations and data processing work across industries.
This course builds programming and automation foundations. Specialist back-end engineering, data science and machine learning roles require additional skills and experience.
Build the server-side systems behind websites and applications. Learn how to design APIs, store data, manage users and deliver working services using Python with Django and JavaScript with Node.js.
Aspiring back-end developers, junior programmers, graduates and developers who want practical experience building web services.
Basic Python and JavaScript knowledge is recommended. Learners use both languages to build and compare working back-end applications.
Practical work: Send requests to a sample API, inspect its responses and explain what the browser needs from the server.
Practical work: Complete Xellux's Basic Hello Web App and Displaying Hyperlinks assignments. Create pages that respond to different URLs and display supplied values.
Practical work: Create product and customer models, add records through the admin, then retrieve and filter them in views.
Practical work: Complete the existing Xellux Creating a Website, Contact Us Form and Functional Email assignments.
Project output: A functional Django website with database-backed content, forms and a managed admin interface.
Practical work: Build and test a product API that supports CRUD operations and rejects invalid records.
Practical work: Rebuild the product API using Node.js and Express, then compare its behaviour with the Django version.
Practical work: Store customers, orders and products in a relational database and retrieve an order with its associated records.
Practical work: Restrict order management to authorised staff while allowing customers to view only their own records.
Practical work: Test a back-end application locally, deploy a working version and verify its main routes and permissions.
Build a back-end system for a business that manages products, customers and orders. Implement the core workflow with Django, then build a matching API workflow in Node.js.
Portfolio output: A Django application, a Node.js API implementation, documented endpoints and a demonstration of the working business workflow.
Internship and junior back-end development, API development and web application support roles in software teams across industries.
The course develops practical foundations in Django and Node.js. Production systems require ongoing work in testing, security, deployment and team development practices.
Learn to turn a real question into a testable machine learning problem. Prepare data, train models, evaluate their results and explain when a prediction is useful or unreliable.
Learners with basic Python knowledge who want to develop practical skills in prediction, classification and data-driven modelling.
Introductory machine learning. Learners should be comfortable with Python functions, data structures and reading tabular data.
Practical work: Examine three real-world questions and decide whether each calls for classification, regression, clustering or a simpler non-ML solution.
Practical work: Prepare a customer dataset containing missing fields, inconsistent categories and numerical values on different scales.
Practical work: Predict sales revenue from supplied business data, compare the model with a simple baseline and explain where its errors are largest.
Project output: A working prediction notebook and a short report interpreting its accuracy and limitations.
Practical work: Compare classification models for an employee turnover dataset. Explain which errors matter most for the stated business question.
Project output: A classification model and an evaluation explaining its usefulness, risks and limitations.
Practical work: Improve a model that performs well during training but poorly on unseen records. Show what changed and whether the improvement holds.
Practical work: Segment customer records and compare the groups using measures that matter to the business.
Project output: A working image-compression demonstration and an explanation of the quality trade-off.
Practical work: Train a small text classifier and review examples it gets wrong. Explore a guided handwritten digit recognition demonstration.
Project output: A sentiment classification workflow with a clear evaluation and error review.
Practical work: Save a model, load it in a separate program and test it with new inputs, including invalid values.
Use a supplied training dataset to investigate whether a model can identify patterns associated with loan default. Treat the output as a modelling exercise, not an automatic lending decision.
Portfolio output: A working modelling pipeline and a presentation explaining its evaluation, limitations and appropriate use.
Junior-level support work in applied machine learning, predictive analytics, data science teams and software products that use trained models.
This course builds practical modelling foundations. Specialist machine learning engineering and production-scale AI systems require additional software engineering, mathematics and experience.
Build applications that use AI to work with information, answer questions and support real workflows. Learn to connect models to software, ground answers in trusted content and test whether the system performs reliably.
Python developers, aspiring AI application developers and technical professionals who want to build useful AI-enabled software.
Basic Python programming and API knowledge are recommended. This course focuses on engineering applications with existing models.
Practical work: Map the steps of a document-question application, including where it obtains information and how a user checks its answer.
Practical work: Build a small application that accepts a question, returns an AI response and handles invalid input or a failed model request.
Practical work: Create an information-extraction workflow that turns varied customer requests into structured records and flags missing fields for review.
Practical work: Build a question-answering application over supplied training documents. Require it to show supporting passages or state when it cannot find an answer.
Project output: A working document assistant that can explain where its answers came from.
Practical work: Build a customer-support workflow that retrieves an order's status from a test database and explains the result to the user.
Practical work: Test the document assistant with a set of known questions. Find failures, improve one part of the workflow and check whether the results improve.
Practical work: Test whether an unauthorised user can retrieve a protected document or persuade the application to ignore its instructions. Correct the application behaviour.
Practical work: Deploy a small AI application, test its main workflows and review logs from both successful and failed requests.
A business needs staff to find reliable answers across its operating guides and retrieve approved information from a test system.
Portfolio output: A working AI application, documented test cases and a demonstration showing how it answers, retrieves and handles uncertainty.
Internship and junior work in AI application development, software engineering teams building AI features, workflow automation and technical product development.
This course builds the ability to integrate and evaluate existing AI models in applications. Training large models and specialist machine learning research require additional study and experience.
Choose the professional direction you want to build towards.
Each programme connects foundational skills, applied training, substantial projects and portfolio development into a structured career pathway.
Learn to turn organisational data into reliable analysis, decision-ready reporting and reusable data systems.
Graduates, career changers, business professionals and learners who want to work in data analysis, business intelligence or analytics engineering.
Beginner. The programme progresses from spreadsheet and business-data foundations to SQL, Power BI, Python, statistics and analytics engineering.
Applied project: Build an executive sales and profitability dashboard from transaction-level data.
Evidence of capability: A working Excel dashboard, defined measures, checked totals and a concise explanation of the business decisions the report supports.
Applied project: Investigate customer and product performance using related database tables.
Evidence of capability: SQL queries, documented assumptions, verified measures and an explanation of the findings.
Applied project: Build a customer or product performance report using SQL and Power BI.
Evidence of capability: A documented data model, tested DAX measures and a dashboard that answers specific management questions.
Applied project: Create a Python-based data cleaning and automated reporting workflow.
Evidence of capability: Reusable code, documented input requirements, output checks and a report that can be updated when new data arrives.
Applied investigation: Determine whether a decline in customer activity is widespread or concentrated in particular products, locations or periods.
Evidence of capability: A clearly stated question, defensible method, checked results and a recommendation that does not claim more than the evidence supports.
Applied project: Build a tested, reusable sales and customer reporting model that supports multiple dashboards.
Evidence of capability: SQL transformations, data-quality checks, metric definitions, version history and documentation for the next analyst.
Portfolio standard: Each selected project must demonstrate independent problem-solving, checked results, clear documentation and the ability to explain its business value.
A growing organisation holds sales, customer, product and operational data across spreadsheets and database tables. Its leadership needs to understand what is changing, what may be driving the change and where to act.
Final portfolio output: A documented analytical workflow, tested SQL assets, Python analysis, executive dashboard, recommendation memo and project presentation.
Internship and junior entry points: Data Analyst, Reporting Analyst, Operations Analyst, MIS Analyst and junior Business Intelligence Analyst.
Further career directions: Business Analyst, Financial Data Analyst, Marketing Analyst, Product Analyst and Analytics Engineer. Senior analytical roles require relevant professional experience.
Banking, fintech, telecommunications, retail, FMCG, consulting, healthcare, insurance, logistics, technology, energy, government and remote or international analytics teams.
Design and build complete software products, from accessible interfaces and reliable back-end systems to deployed applications with useful AI features.
Aspiring developers, graduates, career changers and learners who want to build web applications, software products and AI-enabled services.
Beginner. Learners begin with web and programming fundamentals, then progress through application engineering, deployment and applied AI.
Applied project: Build a responsive business website with accessible navigation and a working enquiry form.
Evidence of capability: A deployed website, source code, responsive layouts and a clear project README.
Applied project: Create an interactive client portal that retrieves records, filters results and lets users complete a defined workflow.
Evidence of capability: A usable React application with tested interactions and clear handling of API errors.
Applied project: Build and document an API for a booking or inventory system, including user roles, validation and automated tests.
Evidence of capability: A working API, test results, endpoint documentation and a short explanation of its security choices.
Applied project: Build an authenticated full-stack platform with React, an API and PostgreSQL. Users must be able to create, view, update and manage records.
Evidence of capability: A working application, schema diagram, database migrations, tests and documented design decisions.
Applied project: Deploy an API-backed application with a database, automated checks, monitoring and a documented release process.
Evidence of capability: A live application, deployment documentation, passing checks and a demonstration of how a failure is detected and investigated.
Applied project: Build a knowledge assistant that answers questions from a defined document collection, cites the retrieved material and declines questions it cannot support.
Evidence of capability: A working application, retrieval design, evaluation cases, documented limitations and basic security and cost controls.
Portfolio standard: Projects must demonstrate functioning software, readable code, tests, documentation and the ability to explain what was built and why.
Design a useful software product for a defined group of users. Build its core workflow first, then add an AI feature that helps users complete a real task within that product.
Final portfolio output: A live full-stack AI application, source repository, architecture diagram, tests, evaluation results, deployment guide and product demonstration.
Internship and junior entry points: Junior Front-End Developer, Junior Back-End Developer, Junior Full-Stack Developer, Web Application Developer and Software Engineering Intern.
Further career directions: Full-Stack Engineer, Cloud Application Developer, AI Application Developer, AI Integration Engineer and Technical Product Engineer. Specialised production roles require further experience.
Software product companies, fintech, e-commerce, EdTech, HealthTech, telecommunications, enterprise technology, start-ups, consultancies and international or remote software teams.
Learn to investigate uncertainty, build predictive models and test whether those models are reliable enough to inform real decisions.
Learners interested in mathematics, statistics, research, forecasting, machine learning and evidence-based prediction.
Foundational Python and mathematics are taught before advanced modelling. Learners should be prepared to practise quantitative reasoning throughout the programme.
Applied work: Investigate a customer dataset containing incomplete records, inconsistent categories and changing behaviour over time.
Evidence of capability: A documented notebook, cleaned dataset, validation checks and an exploratory report.
Applied investigation: Assess whether a change in customer response is credible or could reasonably be explained by sampling variation.
Evidence of capability: A written analysis stating the question, assumptions, method, uncertainty and decision implications.
Named project: Customer churn prediction with an explicit business definition of churn and a comparison against a baseline model.
Evidence of capability: A tested modelling pipeline, evaluation report and explanation of where predictions may be unreliable.
Named project: Develop a segmentation or recommendation prototype, explain its assumptions and demonstrate how its output would be used.
Evidence of capability: Comparative experiments, a selected model or method, evaluation results and a defensible explanation of the choice.
Named projects: Build a demand or revenue forecast, then complete a separate fraud or anomaly investigation using labelled outcomes where available.
Evidence of capability: Time-aware evaluation, error analysis and a report explaining the cost and limits of the proposed decisions.
Applied project: Turn a tested predictive model into a small, documented service with an API and monitoring plan.
Evidence of capability: Model code, test results, API demonstration, model documentation and an explanation of when the system should not be relied upon.
Portfolio standard: A reviewer should be able to inspect the method, reproduce the main results and understand the practical value and limits of each project.
Choose a decision with a measurable outcome, such as customer retention, demand planning or operational risk. Build a predictive approach and test whether it performs well enough to support that decision.
Final portfolio output: A documented dataset and modelling workflow, evaluation report, working demonstration, technical write-up and presentation explaining whether the model should be used.
Internship and junior entry points: Data Science Intern, Junior Data Scientist, Research Data Analyst, Statistical Analyst and Junior Predictive Analyst.
Further career directions: Data Scientist, Forecasting Analyst, Risk Modelling Analyst, Machine Learning Analyst and Applied AI Specialist. Machine Learning Engineer roles usually require deeper production engineering experience.
Banking, fintech, insurance, telecommunications, retail, healthcare, energy, logistics, research, consulting and international or remote teams working on prediction and experimentation.
Connect accounting records, financial models and analytical tools to explain performance, investigate risk and support better financial decisions.
Accounting and finance students, accountants, auditors, banking professionals, finance graduates and professionals who work with financial records or management reports.
Begins with professional Excel and accounting-data foundations. Financial statements and modelling are developed progressively through applied work.
Applied work: Prepare a structured monthly finance workbook from sales, expense and payment records.
Evidence of capability: A reviewable workbook with controlled assumptions, verified totals and a management-ready summary.
Named project: Build an accounts receivable and payable ageing analysis with an exception register and follow-up recommendations.
Evidence of capability: Ageing schedules, reconciled totals, investigated exceptions and documented checks for the reviewer.
Named project: Build a three-statement financial model with a cash-flow forecast and scenarios for changing sales and payment terms.
Evidence of capability: An integrated model, reconciled statements, documented assumptions and a short explanation of financial risks.
Applied project: Replace a manual monthly reporting process with a refreshable Excel and Power Query workflow.
Evidence of capability: A working refresh process, measure definitions, reconciliation checks and handover instructions.
Named project: Build a budget-versus-actual dashboard with variance commentary and a cash-flow management view.
Evidence of capability: A checked financial data model, interactive Power BI report and management commentary explaining material movements.
Named project: Create a finance reporting workflow that uses SQL, Power Query and Power BI to produce a checked monthly management pack.
Evidence of capability: Queries, transformation steps, controls, refresh instructions and verified outputs.
Portfolio standard: Each project must show traceable figures, documented assumptions, professional judgement and an explanation of its practical financial value.
A growing organisation needs an integrated view of performance, working capital and future cash needs. Its accounting records, budgets and operational data are held in different systems.
Final portfolio output: A reconciled financial model, cash-flow forecast, variance report, finance dashboard, controls record and management presentation.
Internship and junior entry points: Finance Analyst Intern, Junior Financial Analyst, Accounting Analyst, Junior Budget Analyst, Finance Reporting Analyst and Audit Data Analyst.
Further career directions: FP&A Analyst, Commercial Finance Analyst, Finance BI Analyst, Treasury Analyst and management accounting roles. Some positions require professional accounting qualifications or additional experience.
Banking, accounting and audit firms, fintech, insurance, FMCG, manufacturing, energy, telecommunications, corporate finance, consulting and international finance teams.
Build the systems that store, protect, move and deliver reliable data for applications, analysts and organisations.
Learners interested in databases, SQL, back-end systems, cloud technology, data pipelines and the reliability of organisational information.
Beginner. The programme begins with relational data and SQL, then progresses to database operations, performance, warehousing and cloud data systems.
Applied work: Query a multi-table order system to answer customer, product and fulfilment questions.
Evidence of capability: Documented SQL scripts, checked outputs and clear explanations of joins and calculations.
Named project: Design and build a normalised enterprise database for customers, orders, products and payments.
Evidence of capability: An ER diagram, schema scripts, sample records, constraint tests and a documented explanation of design choices.
Named project: Secure a database for different user roles, then perform a documented backup and recovery exercise.
Evidence of capability: Permission tests, backup files, restore results and a recovery runbook.
Named project: Diagnose a slow transactional database workload and improve it without breaking correct results.
Evidence of capability: Original and revised query plans, timed comparisons, correctness checks and a change report.
Named project: Build a repeatable pipeline that moves operational records into a warehouse model for sales and customer analysis.
Evidence of capability: Pipeline code, quality checks, a dimensional model and source-to-output documentation.
Applied project: Deploy a small managed database environment with secure access, monitoring and a tested recovery plan.
Evidence of capability: Configuration record, access tests, recovery results and a cost-aware operating guide.
Applied project: Build a scheduled pipeline that ingests new records, transforms them and alerts the operator when a quality check fails.
Evidence of capability: Pipeline code, tests, a run log, failure investigation and an operational handover document.
Portfolio standard: A reviewer should be able to inspect the design, run the core workflow, understand its controls and see evidence that recovery was tested.
Design a data environment for an organisation that processes transactions and needs dependable analytical reporting.
Final portfolio output: A working database, documented schema, secure access configuration, tested recovery process, data pipeline, analytical model and technical architecture presentation.
Internship and junior entry points: SQL Developer Intern, Junior SQL Developer, Junior Database Developer, Database Support Analyst and Junior ETL Developer.
Further career directions: Database Administrator, Database Engineer, Data Engineer, Cloud Database Engineer, Data Platform Engineer and Database Reliability Engineer. Senior infrastructure positions require operational experience.
Banking, fintech, telecommunications, enterprise IT, government, e-commerce, healthcare technology, energy, cloud services and international data teams.
Build useful mobile products that work for real people: intuitive on a phone, dependable on changing networks and ready to improve after release.
Aspiring app developers, designers moving into development, career changers and learners who want to build mobile products for consumers, organisations or their own ventures.
Beginner. Learners develop programming and interface foundations before progressing to mobile architecture, device features, AI integration and product release.
Applied work: Build a small mobile utility that accepts user input, saves a result and handles invalid or incomplete entries.
Evidence of capability: A working app, readable code and a short demonstration on a real device.
Applied project: Design and build a mobile service app through which a user can find information and complete a defined task.
Evidence of capability: Screen flow, working navigation, accessibility checks and documented changes made after user feedback.
Applied project: Build a mobile records app in which users create, edit, search and retrieve their information across sessions.
Evidence of capability: A functioning data workflow with tests for saving, retrieval, error handling and recovery.
Named project: Build a field-work app that records a report and photo offline, then sends the record when connectivity returns.
Evidence of capability: A working offline workflow, synchronisation tests and a demonstration of how the app handles an interrupted upload.
Applied project: Extend the records app with user accounts, role-aware access and a secure API.
Evidence of capability: Authentication tests, documented permissions, privacy decisions and working recovery from an API failure.
Named project: Add an AI-assisted feature that helps a user summarise a field report, categorise an issue or find relevant guidance.
Evidence of capability: A working feature, representative evaluation cases, human-review flow and documented failure behaviour.
Applied work: Prepare a release candidate and run a structured test with users on supported devices.
Evidence of capability: Test record, release build, known-issues list, privacy notes and an improvement plan.
Portfolio standard: Each app must solve a defined user problem and demonstrate working software, sound decisions, tested behaviour and clear documentation.
Identify a real problem faced by a specific group of people. Build a mobile app they can use to complete an important task, including when connectivity is unreliable.
Final portfolio output: A working mobile app, source repository, user-flow documentation, test record, AI evaluation, release build and product demonstration.
Internship and junior entry points: Mobile Development Intern, Junior React Native Developer, Junior Mobile App Developer, Junior Application Developer and Mobile Product Engineering Intern.
Further career directions: Mobile Software Engineer, Cross-Platform Developer, AI-Enabled App Developer, Mobile Product Engineer and Technical Product Builder. Specialist roles require additional delivery experience.
Consumer applications, fintech, healthcare, education, logistics, field operations, commerce, enterprise mobility, start-ups and international mobile product teams.
What could change in your life if you became someone others could depend on to do this work?
One day, the question will no longer be which course you studied. Someone will place a real problem in front of you and ask what you can do.
A business may need to understand where its money is going. A patient may need an application that works when the connection fails. A team may need information it can trust before making a decision. Behind every project is a person affected by whether the work is done well.
That is why the exercises matter. Each time you investigate an error, improve a design, check a result or try again, you are preparing for work that extends beyond the classroom.
You can explain the problem. You can show what you built. You can demonstrate that it works. You can say what still needs improvement.
Your ability has become visible.That ability can become an entry point into a career, a better position at work, a service people need or the foundation of something you build yourself. Over time, it may help you earn more, make steadier choices and support the people who rely on you.
None of those outcomes begins with a title. They begin with the decision to practise until you can take responsibility for the result.
The future does not need you to know everything today. It asks what you are willing to learn, build and become capable of doing well.
Xellux Technologies is a Top ICT Training Company For Global Students. Our Students Work in Top International Companies in Canada, USA, UK, Cyprus, Turkey and Nigeria.
