Lebanese American University

What you'll study

The programme combines core training in statistics, programming and data management with applied modules on machine learning, big data technologies and data visualisation. Teaching typically covers data preparation and cleaning, exploratory data analysis, predictive modelling, supervised and unsupervised learning, time-series analysis, and scalable data processing.

• Programming and Tools: Python and R for data analysis, SQL for databases, and introductions to distributed frameworks such as Apache Spark.
• Statistical Foundations: probability, inferential statistics, regression analysis and experimental design for sound analytics practice.
• Machine Learning: classification, regression, ensemble methods, model evaluation and regularisation techniques.
• Big Data & Cloud: architectures for large-scale data storage and processing, cloud-based deployment and workflow orchestration.
• Data Visualisation & Communication: dashboard design, storytelling with data and tools such as Tableau or equivalent libraries.
• Ethics, Privacy & Governance: issues around data protection, bias in algorithms and responsible use of analytics.
• Capstone / Research Project: an applied project or thesis that integrates methods learned on a real dataset, often undertaken with an industry or organisational partner.

Programme delivery is a mixture of lectures, practical lab sessions, group projects and a significant applied capstone or thesis component. Electives allow specialisation in areas such as natural language processing, advanced visualisation, or business analytics depending on student interests and staff expertise.

Entry requirements

Applicants are normally required to hold a recognised bachelor's degree in computer science, information systems, mathematics, statistics, engineering, economics, or a related discipline. Candidates with a different background but with demonstrable quantitative skills and relevant work experience may also be considered.

• Official transcripts from previous higher education.
• A personal statement outlining motivation and relevant experience.
• A current CV or résumé showing academic and professional background.
• References (usually academic or professional).
• Proof of English language proficiency for applicants whose prior degree was not taught in English; common evidence includes recognised tests or equivalent institutional certification.
• Depending on the applicant, an interview or assessment task may be invited to evaluate preparedness for the programme.

Prior programming experience and basic exposure to statistics are advantageous; preparatory courses or bridging modules may be recommended for those lacking specific prerequisites.

Career prospects

Graduates go on to roles across private, public and non-profit sectors where data-driven decision making is required. Common job titles include data analyst, data scientist, machine learning engineer, business intelligence analyst, analytics consultant and data engineer.

• Industry sectors: finance and banking, healthcare, telecommunications, retail and e‑commerce, energy, consulting and government agencies.
• Typical employers: corporate analytics teams, specialised analytics consultancies, startups, research institutes and public-sector analytics units.
• Career progression: starting in technical analytics roles, graduates frequently move into senior data science positions, analytics management, product analytics or strategy roles where they combine technical expertise with domain knowledge.

Why study at Lebanese American University

Lebanese American University offers an American-style curriculum with an emphasis on applied learning and industry relevance. The programme is delivered by faculty with expertise in computing, statistics and applied analytics, and makes use of modern computer labs and software tools commonly used in the analytics industry.

• Applied focus: coursework and capstone projects emphasise real-world datasets and collaboration with industry partners where possible.
• Flexible pathways: options to tailor studies through electives and specialised projects to match career objectives in business analytics, machine learning or data engineering.
• Regional and international links: students benefit from LAU's connections with employers and alumni across the region, supporting internship and employment opportunities.
• Student support: academic advising, career services and technical workshops help transition graduates into analytics roles.