Business Administration
Bachelor’s in Business Management and Organisational Leadership
A pathway connecting core business functions with leadership, planning, communication and decision making through organisational challenges.
Technology and Data
Bachelor’s in Artificial Intelligence and Data Analytics
بكالوريوس الذكاء الاصطناعي وتحليل البيانات
Programme overview
A pathway connecting programming, statistics, data management and machine learning to frame problems and develop responsible, testable analytical solutions.
This pathway begins with mathematical and computing foundations before progressing to data collection, preparation, analysis and artificial intelligence models. It connects data quality with the validity of conclusions and emphasises interpretation, privacy, bias and the limits of models. An applied capstone brings these strands together through a clear account of the problem, method, results and recommendations for technical and non-technical audiences.
Who is this programme for?
- Learners interested in programming, mathematical reasoning and solving problems with data.
- Those seeking an academic foundation for analytical roles and intelligent applications.
- Learners exploring data applications in business, services and research.
Programme objectives
- Build connected foundations in programming, statistics and artificial intelligence.
- Develop the ability to select analytical methods that fit the question and data.
- Establish documentation, reproducibility and responsible data use.
- Translate technical findings into clear recommendations for decision making.
Learning outcomes
- Prepare data from multiple sources and document its quality and limitations.
- Write understandable, reviewable analytical programs and database queries.
- Compare machine learning models using suitable data splits and evaluation metrics.
- Create visualisations and dashboards that communicate findings and uncertainty faithfully.
- Assess privacy and bias risks and identify proportionate mitigations.
- Complete an analytical project with a report explaining decisions and results.
Programme modules
01Programming and Computational Thinking
Frame problems as clear steps and build structured programs for processing data.
Key topics
- Programming foundations and functions
- Data structures and algorithms
- Testing and version control
Learning outcomes
- Develop and test a documented program that processes a dataset.
02Mathematics and Statistics for Analytics
Quantitative foundations for understanding patterns, variation and the limits of conclusions.
Key topics
- Linear algebra and calculus concepts
- Probability and distributions
- Descriptive and inferential statistics
Learning outcomes
- Select appropriate summaries and statistical tests and explain their assumptions.
03Databases and Data Engineering
Organise data and design a dependable route for collecting, transforming and preparing it for analysis.
Key topics
- Relational modelling and SQL queries
- Data integration and cleaning
- Data quality and provenance
Learning outcomes
- Build a database and processing pipeline with data quality checks.
04Exploratory Analysis and Visualisation
Explore data and turn relationships and indicators into visual explanations of a defined question.
Key topics
- Patterns and outliers
- Chart selection and performance indicators
- Dashboards and analytical storytelling
Learning outcomes
- Produce exploratory analysis and a dashboard that explains findings and their limits.
05Machine Learning and Model Evaluation
Build predictive models and compare them with suitable baselines while preventing data leakage.
Key topics
- Supervised and unsupervised learning
- Feature engineering and cross-validation
- Overfitting and performance metrics
Learning outcomes
- Compare models and justify a choice using performance and interpretability.
06Artificial Intelligence Applications
Explore text, image and generative applications while assessing their usefulness and limitations.
Key topics
- Neural network foundations
- Language processing and computer vision
- Evaluating and verifying generative outputs
Learning outcomes
- Develop an intelligent application prototype and assess its errors and fitness for use.
07Data Governance and Responsible AI
Examine the human and organisational consequences of data collection and model use.
Key topics
- Privacy and access management
- Bias, fairness and explainability
- Monitoring, documentation and human oversight
Learning outcomes
- Prepare a risk assessment and monitoring plan for a model in a defined context.
08Applied Data Analytics Capstone
Integrate analytics and programming in an inspectable solution to a clearly defined problem.
Key topics
- Question framing and data planning
- Implementation, validation and experiment records
- Reporting, presentation and recommendations
Learning outcomes
- Deliver a reproducible project with a critical account of impact and limitations.
Assessment
- Assignments
- Projects
- Research or final project
Career opportunities
- Data analysis and business intelligence
- Data application development
- Machine learning project support
- Data quality and reporting
Frequently asked questions
Is the pathway limited to using AI tools?
No. It covers programming, mathematics, data and model evaluation so that learners can understand outputs, verify them and build solutions beyond direct tool use.
What is the capstone intended to involve?
It addresses a defined question using suitable data and includes preparation, analysis or modelling, evaluation and reporting.
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