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.

Mission

The MS in Data Analytics at the Lebanese American University is to equip students with the knowledge, technical skills, and critical thinking required to collect, manage, analyze, and interpret large-scale data. The program emphasizes both theoretical foundations and practical applications, fostering ethical and innovative use of data to support strategic decision-making and organizational growth.

Program Objectives

The MS in Data Analytics combines advanced analytical methods with strategic decision-making to prepare graduates for data-driven leadership. The program emphasizes hands-on learning through specialized courses in machine learning, data mining, predictive modeling, and big data technologies, using industry-standard tools such as Python, R, SQL, and Tableau.

Students engage in applied research and real-world projects across industries like finance, healthcare, marketing, and supply chain management, building strong portfolios and industry collaborations. A required Capstone Project further integrates theory and practice by tackling real business challenges under faculty mentorship.

Graduates are equipped with the technical expertise, critical thinking, and communication skills to transform data into actionable insights. They are well-prepared for careers in data science, business intelligence, financial analytics, and AI-driven decision-making, bridging the gap between raw data and business strategy in today’s digital economy.

Learning Outcomes

Upon completion of the MS in 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.

Admission Requirements

Admission to the graduate programs offered at the Adnan Kassar School of Business follows the LAU general graduate requirements.

In addition, applicants to the program must satisfy the following requirements:

• Educational Background: A bachelor’s degree in a related field (e.g., business, computer science, engineering, mathematics, or statistics) from an accredited institution.
• Quantitative & Technical Proficiency: Prior coursework in statistics and programming is expected (Python or R recommended). Applicants lacking these may join a preparatory bootcamp.
• Academic Performance: A minimum cumulative GPA of 3.2.

Graduate Assistantship

Funding in the form of graduate assistantships pays for tuition in part or in full, in exchange for research or teaching support for faculty. All prospective students who register for six credits a semester are eligible to apply for assistantships. More information is found in the Graduate Academic Rules and Procedures.

Steps for New Students:

1. Apply to the MS in Data Analytics program and pay the $40 application fee (online or in cash).
2. Activate your Portal after receiving login details via email (within 3 working days).

GA Application Requirements (All Students):

• Complete and submit the GA application via Portal.
• Transcripts:
  • Non-LAU students: official transcript
  • LAU students: unofficial transcript
  • Current students:
    • <12 credits: undergraduate + graduate transcripts
    • ≥12 credits: unofficial graduate transcript only
• Recent NSSF work certificate (less than 1 month old).
• Updated CV.

Curriculum

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 Courses (9 credits)

Core Requirements (21 credits)

• DAN604 Statistics for Data Analytics (2 cr.)
• DAN611 Applied Machine Learning (3 cr.)
• DAN612 Data Ethics (1 cr.)
• DAN613 Data Engineering (1 cr.)
• DAN614 Data Visualization (2 cr.)
• DAN623 Natural Language Processing with Text Analytics (3 cr.)
• DAN691 Decision Making with Data (3 cr.)
• DAN697 Capstone (3 cr.)
• DAN698 Project (3 cr.) or DAN699 Thesis (6 cr.)

Elective Courses (9 credits with a minimum of 6 credits in Data Analytics)

Recommended Study Plan

A typical study plan spans two years (five semesters – Fall, Spring, Summer, Fall, and Spring) with a balanced mix of core courses, electives, and a culminating project:

Year One

Fall (6 Credits)

• DAN601 Decision Making with Data (3 Credits)
• DAN604 Statistics for Data Analytics (2 Credits)
• DAN612 Data Ethics (1 Credit)

Spring (6 Credits)

• DAN611 Applied Machine Learning (3 Credits) 
• DAN614 Data Visualization (2 Credits)
• DAN613 Data Engineering (1 Credit)

Summer (Choose One Option)

Thesis Option (6 or 9 Credits)

• DAN699 Thesis in Data Analytics (6 Credits)
• Elective (3 Credits, optional)

Non-Thesis Option (6 or 9 Credits)

• DAN697 Capstone Project (3 Credits)
• DAN698 Research Project in Data Analytics (3 Credits)
• Elective (3 Credits, optional)

Year Two

Fall (6 Credits)

• DAN623 Natural Language Processing with Text Analytics (3 cr.)
• Elective (3 Credits)

Spring (6 Credits)

• Elective (3 Credits)
• Elective (3 Credits)