Master of Science in Data Analytics
Transforming Raw Data into Strategic Business Insights
Location: LAU Beirut
Delivery Mode: On-campus
Duration: two years (full-time)
Start term: Fall or Spring
Total Credits: 30
Department: ITOM
Program Overview
In an era defined by digital transformation, organizations must convert vast amounts of raw data into actionable business insights. The Master of Science in Data Analytics program is designed to equip you with interdisciplinary knowledge and hands-on skills at the intersection of information technology, operations management, applied mathematics, and statistics. By mastering advanced techniques in data mining, machine learning, and statistical analysis—and by emphasizing ethical data practices—you will be prepared to drive innovation and competitive advantage in today’s data-driven world.
Admission
Admission to the Master of Science in Data Analytics program follows the LAU general graduate requirements. In addition to meeting these standards, applicants must fulfill the following criteria:
Educational Background:
Applicants must hold a bachelor’s of science degree from an accredited institution in a related field. Examples of eligible disciplines include, but are not limited to, business, computer science, business, engineering, mathematics, statistics, or any other quantitative field.
Quantitative and Technical Proficiency:
A strong foundation in quantitative methods is essential. Applicants should have completed coursework in statistics and programming (experience with languages such as Python or R is highly recommended, but not obligatory). Applicants lacking these skills may enroll into a bootcamp to enhance their readiness and performance in courses.
Academic Performance:
A minimum cumulative GPA of 3.2 is required for admission.
Additional Considerations:
While not mandatory, relevant professional experience in data analytics or a related area is highly valued and may strengthen an application. Lack of this professional experience will not lower your chances of being admitted to the Program.
Program Requirements
The Master of Science in Data Analytics program consists of 30 credits. The curriculum is designed with core courses that emphasize advanced analytics, machine learning, data mining, natural language processing, and quantitative methods. A selection of diverse topics totaling 9 credits forms the elective component, surveying current issues and emerging technologies in data analytics across local, regional, and international contexts.
To obtain the MS degree, students must complete a total of 30 credits composed of:
- Core Requirements (21 credits)
- Elective Requirements (9 credits)
Core Requirements (21 credits)
| # of Credits | Course Name | Course Number |
|---|---|---|
| 3 | Decision Making with Data | DAN 601 |
| 2 | Statistics for Data Analytics | DAN 604 |
| 1 | Data Engineering | DAN 613 |
| 2 | Data Visualization | DAN 614 |
| 1 | Data Ethics | DAN 612 |
| 3 | Applied Machine Learning | DAN 611 |
| 3 | Natural Language Processing with Text Analytics | DAN 623 |
| 3 | Capstone | DAN 697 |
| 3 | Project | DAN 698 |
| OR | ||
| 6 | Thesis | DAN 699 |
Electives (9 credits with a minimum of 6 credits in Data Analytics)
| Cognitive Analytics | DAN 615 |
|---|---|
| Research Methods in Data Analytics | DAN 696 |
| Analytics Applications | DAN 642 |
| Information Security User Behavior Analytics | DAN 617 |
| Healthcare Analytics | DAN 618 |
| Big Data Processing and Blockchain Technology | DAN 619 |
| Analytical Data Mining | DAN 634 |
| Data Management for Analytics | DAN 635 |
| Customer Behavior Analytics | DAN 636 |
| Web and Social Media Analytics | DAN 637 |
| Supply Chain Analytics | DAN 638 |
| Business Analytics for Competitive Advantage | BDA 811 |
| Forecasting Analytics and Data Mining | BDA 880L |
| Reinforcement Learning | DAN 624 |
| Sp. Topics in Data Analytics | DAN 630 |
| Artificial Intelligence for Managers | BDA 625 |
Program Goals and Outcomes
The MS in Business Data Analytics places a strong emphasis on hands-on research and practical applications, equipping students with the necessary skills to analyze, interpret, and apply data-driven insights in a variety of business contexts. Our program goes beyond teaching technical skills by integrating advanced analytical methods with strategic decision-making.
Comprehensive data analytics training is a core component of the program. With specialized courses in machine learning, data mining, predictive modeling, and big data technologies, students gain a robust understanding of advanced analytical techniques. Throughout their coursework, students learn to use industry-standard tools such as Python, R, SQL, and Tableau to manipulate datasets, develop predictive models, and derive actionable business insights.
The real-world application of analytics is integral to our approach. Students engage in diverse research projects, applying machine learning algorithms, statistical models, and optimization techniques to solve business challenges across industries such as finance, healthcare, marketing, and supply chain management. These projects allow students to build a strong portfolio, explore specialized areas of interest, and collaborate with industry partners.
To graduate, students must complete a Capstone Project, conducted under the supervision and mentorship of experienced faculty. This project allows them to synthesize and apply their knowledge to a real-world business problem, from defining the problem statement to deploying a data-driven solution. Many students produce industry-relevant research reports and, in some cases, contribute to peer-reviewed publications or company-commissioned projects.
This focus on practical, hands-on experience ensures that our graduates are well-prepared for careers in data science, business intelligence, financial analytics, and AI-driven decision-making. Upon graduation, students are proficient in working with complex datasets, developing predictive models, conducting rigorous research, and effectively communicating findings—highly valuable skills across industries.
By integrating technical expertise with business strategy, our graduates are equipped to drive data-driven transformation within organizations, bridging the gap between raw data and actionable business intelligence. Whether pursuing careers in industry or academia, our alumni are well-positioned to lead analytics-driven decision-making in today’s digital economy.
Program Learning Outcomes
Upon completion of the MS in Business Data Analytics, graduates will be able to:
- Demonstrate advanced knowledge in data analytics, machine learning, and statistical modeling.
- Apply predictive modeling and data mining techniques to extract insights from large datasets.
- Utilize business intelligence and data visualization tools to support strategic decision-making.
- Implement quantitative methods and optimization techniques for business problem-solving.
- Design, develop, and deploy data-driven solutions to real-world business challenges.
- Interpret and critically evaluate academic and industry research in data analytics.
- Assess the impact of data-driven decision-making on business strategy and performance.
- Communicate complex analytical findings effectively to technical and non-technical audiences.
- Apply ethical considerations and best practices in handling and analyzing data.
- Lead and manage data analytics projects, demonstrating strong problem-solving and teamwork skills.