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School of Computing Skyling University

Graduate Programs

Master of Science in Data Analytics

Overview

The discipline of data analytics comprises of the processes, technologies, and methodologies used to collect, store, process, analyze, and visualize data in a manner that enables decision-makers to make informed decisions. Data analytics involves the use of advanced analytics techniques, such as predictive modeling, machine learning, and data mining, to identify patterns and relationships in data. These techniques enable organizations to understand customer behavior, optimize supply chain management, improve product design, and enhance overall business performance.
The Master of Science in Data Analytics program offered at the School of Computing at Skyline University College can be distinguished from other similar programs by its focus and emphasis on Big data. Big data Analytics is a subset of Data Analytics discipline dealing with Massive sets of complex data that can be collected from hundreds of different sources with different forms and shapes (structured or unstructured). The complexity and sheer size of big data requires specialized infrastructure and different advanced tools and techniques with including both software and hardware such as Hadoop and Spark, which provide scalable and cost-effective solutions for storing and processing large datasets.
The Master of Science in Data Analytics program provides students with the necessary technical and analytical skills to collect, process, analyze, and interpret large and complex data sets. Additionally, students learn how to communicate their findings effectively to key stakeholders and decision-makers.
The curriculum is designed to cover a wide range of topics geared towards Big Data Analytics, including Statistical Analysis, Machine Learning, Data Visualization, and Big Data management. Students also learn about emerging technologies and tools used in big data analytics, such as Hadoop, Spark, and NoSQL databases.
Graduates of this program are well-equipped to work as data scientists, data analysts, or data engineers in various industries, including finance, healthcare, retail, and manufacturing, among others

Program Educational Objectives
The MSDA Program will enable its graduates to: 
1. Work collaboratively and communicate effectively to ensure a superior and productive experience for the user and all the organization’s functions. (Team work and Communication) 
2. Apply knowledge and Skills in Big Data Analytics to have a successful professional career.  (Professional Career) 
3. Use expertise in implementing a wide range of innovative and sustainable Secured Computing solutions to support the community.  (Community support) 
4. Recognize social responsibilities and perform duties professionally and ethically.  (Social Responsibility and Ethics)  
5. Engage in professional and personal development through life-long learning and continuing education.  (Life-Long Learning)

Industries

Business Analyst

Database Administrator

IT Project Manager

Technical Support Specialist

Typical Job Titles

Data Scientist

Big Data Engineer

Data Analyst

Business Intelligence Analyst

Machine Learning Engineer

Database Administrator

Data and Analytics Manager

Financial Analyst

Customer Insights Analyst

Risk Analyst

Healthcare Data Analyst

Social Media Data Analyst

Data Architect

Data Engineer

Quantitative Analyst (Quant)

Analytics Consultant

Data Visualization Specialist

Statistician

AI Specialist

Marketing Analyst

Supply Chain Analyst

Fraud Analyst

Product Analyst

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Financial Support

In 2022, 56% graduate students received financial support towards their tuition fees.

Financial Support Options:

Undergraduate Merit Scholarships

Youth Empowerment Scholarships

Tuition Discount for Sponsored Undergraduates

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Accreditation

Faculty

Prof. Ghassan Issa

Professor Dean School of Computing

Prof. Ammar AlMomani

Professor, Head of Research and Innovation

Dr. Manas Ranjan Pradhan

Associate Professor

Dr. Beenu Mago

Assistant Professor

Dr. Waleed Alomoush

Assistant Professor