Master of Science in Data Analytics

Transforming Raw Data into Strategic Business Insights

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)

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

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.

Courses

Our curriculum is designed to balance theoretical foundations with practical applications. Key new courses include:

DAN 611 – Applied Machine Learning (3 credits)

Students in this course will learn about supervised and unsupervised training methods. The focus is on identifying relationships that cannot be found by basic statistics and used, for example, in customer satisfaction, branding, machine failure, resource allocation, fraud detection, and fraudulent activities. Techniques include Nearest Neighbors, Naïve Bayes, deep learning, text mining, clustering, association rules, regularization, and dimensionality reduction. The bias/variance trade-off and model selection is a focal point of the course and will be illustrated from multiple angles. Students will acquire hands-on experience with all techniques taught​.

Prerequisites: DAN 604

DAN 614 - Data Visualization (2 credits)

This course introduces students to the latest data visualization techniques and tools to visualize data using dashboards, scorecards, and other formats. Students will learn presentation techniques with emphasis placed on the data story, the visual display of data, and smart reporting of results. Students will acquire hands-on skills to create effective presentations leveraging the latest technology and software such as Tableau, QlikView, or IBM Insights. Other covered topics include web analytics and communication​.

Prerequisites: DAN 604

DAN 697 - Capstone Project (3 credits)

The Capstone Project course is a pivotal component of the Master of Science in Business Data Analytics program, designed to synthesize and integrate knowledge acquired throughout the curriculum. This course challenges students to apply their comprehensive data analytics skills to analyze and address real-world business issues. By engaging in a final project, students will demonstrate their capability to develop corporate and business strategies using advanced business analytics techniques​.

DAN 698 - Research Project in Business Analytics (3 credits)

This course entails the application of research methods to a current topic relevant to Business Data Analytics. The project must incorporate the student’s hypothesis, test methods, test results, and conclusions​.

Prerequisites: DAN 696 or Equivalent

DAN 699 - Thesis in Business Analytics (6 credits)

Students pre-approved for a thesis may enroll in this class. Students will write a thesis on a topic related to business data analytics approved by the Thesis Supervisor. Students will conduct their research and write their thesis under the supervision of a full-time faculty member and assisted by two other faculty members​.

Prerequisites: DAN 696 or Equivalent

DAN 623 - Natural Language Processing with Text Analytics (3 credits)

This course focuses on the computational aspect of Natural Language Processing (NLP) technologies and aims at finding a balance between traditional and modern NLP techniques. It covers major concepts and techniques for processing, cleaning, visualizing, and analyzing textual data to extract interesting information, discover knowledge, and support decision-making in business applications. Students will learn fundamental pre-processing techniques (i.e., tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition), text representation (i.e., vector-space and language models, and modern distributed representation of words), and various text analytics tasks (i.e., text categorization and classification, document summarization, and sentiment analysis). Hands-on labs and projects in parallel to course lectures and readings will allow students to develop practical skills in building foundational NLP tools that can be applied to address real-world business analytics problems. 

Prerequisites: DAN 611 

DAN 601 - Decision Making with Data (3 credits)

This course delves into the integral role of data analytics in decision-making processes across diverse sectors. Students will develop a foundational understanding of the significance of data, its evolution within the digital age, and the transformative impact of “Big Data” on modern analytics practices.

Throughout this course, you will: 

• Explore the essentials of data analytics, emphasizing its critical role in supporting informed and strategic decision-making across various industries. 

• Investigate the historical development and contemporary significance of data analytics, with a focus on the advent and integration of “Big Data” technologies. 

• Be introduced to a systematic framework for data analysis, encompassing the predominant tools and techniques that enhance analytical accuracy and efficacy. 

• Apply your knowledge through practical, simulated business scenarios, employing analytical skills to navigate and resolve complex challenges. 

Prerequisites: None

DAN 604 - Statistics for Data Analytics (2 credits)
This course offers an in-depth exploration of statistical methods critical for data analytics within business contexts. Emphasizing practical application, the course covers essential statistical techniques and their role in making informed business decisions. Students will learn to apply methods such as hypothesis testing, regression analysis (both linear and logistic), ANOVA, clustering, and principal component analysis. The course also addresses the use of statistical software for data analysis, interpretation of analytical results, and common pitfalls in data analysis. Through real-world business cases, students will develop the skills to effectively model and analyze business data, enhancing their ability to contribute strategic insights based on robust statistical reasoning.

Prerequisites: None

DAN 613 - Data Engineering (1 credit)

This course provides a concise introduction to Data Engineering with a focus on the ETL (Extract, Transform, Load) process, equipping students with essential skills to source, prepare, and manage data efficiently. Students will learn key concepts of data integration and the basics of both SQL and NoSQL databases, with an emphasis on practical applications in a business context.

Practical exercises will allow students to apply their knowledge to real-world scenarios, preparing them to handle data engineering tasks effectively.

Prerequisites: None

DAN 612 - Data Ethics (1 credit)

This course examines the ethical aspect of business analytics. It covers topics in data ethics regarding the moral obligations, values and principles governing data protection and privacy rights. It also explores how data driven decisions can have a profound impact on society and how businesses can refine their strategy to learn from past mistakes and built on previous successes. 

Prerequisites: None

DAN 615 - Cognitive Analytics (3 credits)

The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.

Prerequisites: None

DAN 696 - Research Methods in Data Analytics (3 credits)

This course offers an in-depth exploration of statistical methods critical for data analytics within business contexts. Emphasizing practical application, the course covers essential statistical techniques and their role in making informed business decisions. Students will learn to apply methods such as hypothesis testing, regression analysis (both linear and logistic), ANOVA, clustering, and principal component analysis. The course also addresses the use of statistical software for data analysis, interpretation of analytical results, and common pitfalls in data analysis. Through real-world business cases, students will develop the skills to effectively model and analyze business data, enhancing their ability to contribute strategic insights based on robust statistical reasoning.

Prerequisites: None

DAN 642 - Analytics Applications (3 credits)

The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.

Prerequisites: DAN 601

DAN 617 - Information Security User Behavior Analytics (3 credits)

This course covers key risks to information systems and business data. Students will apply data analytics techniques across different dimensions to provide effective information security analytics. Threats to normal user behavior are compared and contrasted by utilizing the user behavior analytics approach Normal behavior. 

Prerequisites: None

DAN 618 - Healthcare Analytics (3 credits)

The rise of preventive care, health technology and telemedicine has generated massive amounts of multidimensional health data. The magnitude and complexity of these data are overwhelming for healthcare providers and stakeholders to analyze and extract meaningful knowledge to make informed decisions. Moreover, the COVID 19 pandemic has unveiled profound weaknesses in the healthcare systems of most countries. Global investments in private health systems and private healthcare solutions have witnessed a 6% increase in Q2 2020 and are predicted to increase significantly in the future. The expected digital transformation will not be possible without data and analytics. 

In this course, you will be equipped with the knowledge to work in the healthcare field or with a healthcare client as analyst or consultant. You will be introduced to the pillars of healthcare systems and the main health concepts and measures. You will learn about healthcare data types and sources, how to formulate data queries, how to use geospatial information systems to map health data and how analytics is applied in the healthcare field. Finally, you will dive into the economic evaluation and financial impact of health-related interventions and programs. 

Prerequisites: Dan 614

DAN 619 - Big Data Processing and Blockchain Technology (3 credits)

This course has two pillars. The course first focuses on blockchain technology and its applications in business. It explores how blockchain brings profound changes to businesses and explains how it transforms businesses structures, functions and roles of the organization. The course then dives into the various methods of blockchain governance that exist in the market place and examines specific features of blockchain to overcome problems that have been difficult to solve in the past using the existing centralized architecture. Topics include: key concepts like hashing, public key cryptography, digital signing, mining, proof-of-work, proof of stake, public vs private vs permissioned blockchain, peer-to-peer transactions, blocks, consensus mechanisms, smart contracts, crypto-asset, distributed resources, decentralized protocol, and the double spending problem. These concepts will be illustrated using the Bitcoin application and implemented mainly using Ethereum. The course then tackles how to process large data volumes on large computational clusters by introducing advanced features for Spark 2.0. Students will learn how to set up clusters in both batch and real time modes, retrieve big volumes of textual data, analyze streaming data and use the ML API. 

Prerequisites: DAN 611

DAN 634 - Analytical Data Mining (3 credits)

This course will provide students with an understanding of fundamental data mining concepts and tools. The topics covered include data sources, data cleaning techniques and tools, common data mining algorithms, statistical modeling, and widely used tools for both structured and unstructured data mining. 

Prerequisites: DAN 601

DAN 635 - Data Management for Analytics (3 credits)

The goal of this course is to give students the fundamental knowledge and abilities needed to comprehend and apply data modification and management. They will get practical knowledge and experience in data management for business intelligence, analytics, and data science endeavors. Professionals working in data and analytics will also be able to comprehend the various newly developed data management solutions. 

Prerequisites: DAN 601

DAN 636 - Customer Behavior Analytics (3 credits)

Customer Behavior Analytics is a dynamic course designed to equip students with the advanced analytical skills necessary to transform customer data into actionable insights, driving strategic decisions in marketing and customer service. This course delves into the core principles of CRM, exploring how cutting-edge data analytics can optimize customer interactions and enhance organizational growth. Students will learn to harness the power of both descriptive and predictive analytics to understand customer behaviors, preferences, and trends. 

Prerequisites: DAN 604

DAN 637 - Web and Social Media Analytics (3 credits)

This course addresses the move towards social media to build intellectual capital, communicate with society, exchange knowledge among a global workforce, and provide the public face of business for marketing and corporate communications. The course explores the role of social media technologies (e.g., Twitter) in shaping societal and business trends, and emphasizes analyzing social media data in terms of reach, engagement, influencers, etc. using Python and open source tools. The course also explores social networks in the important of information propagation in social media. 

Prerequisites: DAN 611

DAN 638 - Supply Chain Analytics (3 credits)

This course presents practical applications of data analytics (descriptive, predictive, and prescriptive) in the manufacturing, trade, and service industries, in a range of supply chain management domains, including forecasting and inventory management, sales and operations planning, transportation, logistics, and fulfillment, purchasing and supply management, supply chain risk management, etc. In order to improve supply chain efficiency and business value, students learn how to identify the correct data set, ask the relevant questions, and utilize the right models and tools to create decisions that are based on facts. Product development analytics, inventory and resource management, spend analytics and supplier selection, transportation analytics, fulfillment diagnostics in logistics systems, sales and operations analytics in production, demand forecasting for new products, and supplier and product line selection are among the topics covered. 

Prerequisites: None

BDA811 – Business Analytics for Competitive Advantage (3 credits)

Business Data Analytics (BDA) is emerging as an essential driver of competitive advantage, and in today’s dynamic business environment, success in the market and achieving sustainable competitive advantage require understanding the fundamentals of collecting data, describing and developing insights from data sets, and presenting and communicating results. 

The goal of this course is to equip participants with fundamental data analytics skills needed to optimize business processes and gain insights that inform business decisions. In an era where data are considered a corporate asset, it is crucial to learn how important it is today for decision makers to convert raw data into insight, solve problems, and seize opportunities. Topics include importance of business analytics, types of analytics – descriptive, predictive, and prescriptive -, data visualization, and reporting. Using case studies, group problem solving, and lab sessions, students will get hands-on learning through the deployment of a variety of powerful software and computer-based data analytics and visualization tools, including advanced Excel, SPSS, and Tableau. Students will also learn how to make more powerful presentations by understanding and implementing the key principles of report and visual presentation of data.

Prerequisites: None

BDA880L – Forecasting Analytics and Data Mining (3 Credits)

Time series forecasting is essential for every organization that deals with quantifiable data. It is widely used in retail stores, international financial organizations, energy companies, banks and lending institutions, and in many other industries. Forecasting analytics enable managers and policy makers to better make informed decisions. This course is a hands-on introduction to quantitative forecasting of time series. Students will learn the most popular forecasting techniques used in practice. The course covers topics such as pre-processing, characterization, and visualizing time series, model performance evaluation, smoothing methods, time series regression models, Box-Jenkins models, autoregressive integrated moving average (ARIMA) models, models with binary outcome, and neural networks for time series (if time permits).

Prerequisites: DAN 604

BDA 625 - Artificial Intelligence for Managers (3 credits)

This course equips students with a strategic understanding of artificial intelligence and Machine Learning, preparing them to lead AI-driven initiatives in modern organizations. This course covers core AI concepts, including machine learning, building data models, data pre-processing and analytics with an emphasis on real-world business applications. Students will learn how to identify and evaluate AI opportunities, communicate effectively with technical teams, and make data-informed decisions. Through case studies and practical exercises, this course provides the skills and knowledge managers need to drive value and innovation within their firms.

Prerequisites: DAN 611

DAN 624 – Reinforcement Learning (3 credits)

This course introduces the principles of reinforcement learning (RL) with a focus on optimizing decision-making in complex business environments. Students will explore the core concepts of RL, including goal-oriented algorithms that learn from the outcomes of their actions without human intervention. The curriculum covers finite Markov decision processes, dynamic programming, Monte-Carlo methods, and temporal-difference learning, such as Q-learning. Additionally, the course delves into advanced topics like function approximation and policy gradient methods, with a strong emphasis on real-world business applications such as supply chain optimization, customer experience enhancement, and dynamic pricing strategies. Practical sessions include simulations and case studies where students apply RL techniques to solve business problems, reinforcing theoretical knowledge with actionable insights.

Prerequisites/Co-requisite: DAN 623

DAN630 – Special Topics in Data Analytics (3 credits)

This course is designed to keep pace with the rapidly evolving landscape of data analytics, offering a dynamic curriculum that adapts to new trends, technologies, and methodologies in the field. It provides students with the opportunity to engage with cutting-edge topics selected based on their emerging relevance and potential impact on business and technology. The course format includes guest lectures from industry leaders, hands-on projects, and case studies that explore innovative data analytics applications such as advanced machine learning models, big data solutions, IoT analytics, and ethical AI. Through this course, students will develop a deep understanding of how to strategically apply the latest analytics tools and concepts to solve real-world business challenges, fostering an environment of continuous learning and professional growth.

Prerequisites: None

Typical 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:

Fall 1 (6 Credits)

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

Spring 1 (6 Credits)

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

Summer 1 (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)

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

Fall 2 (6 Credits)

• DAN623 – NLP & Text Analytics (3 Credits)
• Elective (3 Credits)

Spring 2 (6 Credits)

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

Program Totals: 30 Credits

Comprehensive/Culminating Elements:

• Thesis Option: DAN699 - Thesis in Data Analytics
• Non-Thesis Option: DAN698 - Research Project and DAN697 - Capstone Project

Student Research Awards

The program actively supports innovative research through a range of awards and grants. Outstanding research projects are recognized on LAU Research Day, at national and international conferences, offering funding opportunities and increased visibility within the academic and professional communities.

Joint Publications with Faculty Members

Collaboration is key to advancing knowledge in business data analytics. Students often work closely with faculty on research projects, leading to joint publications in reputable academic journals. These partnerships foster a rich learning environment and contribute to the cutting-edge developments in the field.

Alumni Statistics

We aim to help our graduates consistently demonstrate strong outcomes:

• Retention Rate: >=95%
• Graduation Rate: >=90%
• Job Placement Rate: >= 90%
• Average Starting Salary: Competitive across sectors including technology, finance, and healthcare

Alumni Placements

Testimonials