Hasaplaýyş maglumat ylmy

The Double Degree Master of Science in Computational Data Science (M.Sc. CODS) is a flagship graduate program jointly delivered by Khalifa University (UAE) and New Uzbekistan University (Uzbekistan). Designed at the intersection of advanced computing, applied mathematics, and real-world problem solving, the program prepares highly skilled data scientists ready to lead in industry, government, research institutions, and doctoral programs worldwide. Graduates earn two internationally recognized diplomas — one from Khalifa University and one from New Uzbekistan University.

Program Vision

The MSc in Computational Data Science is built to educate the next generation of data scientists who can:

● Design scalable algorithms
● Extract actionable insight from large-scale data
● Build AI-powered systems
● Lead digital transformation initiatives
● Conduct high-level research in computational sciences

Program Educational Objectives

The MSc CODS program aims to produce graduates who:

● Advance professionally and become recognized experts in computational data science.
● Apply advanced technical expertise to solve critical societal and industrial challenges.
● Demonstrate ethical leadership in data-driven decision-making.
● Pursue doctoral education or high-level professional specialization when desired.

Student Learning Outcomes

Upon graduation, students will be able to:

● Formulate and solve advanced computational data science problems.
● Apply mathematical modeling and statistical inference rigorously.
● Design and implement scalable machine learning systems.
● Critically evaluate emerging AI and data technologies.
● Conduct independent research and defend findings in a formal examination.
● Work effectively in multidisciplinary teams.
● Uphold professional and ethical standards in data practice.

Concentration Tracks

Computational Systems Concentration

● Advanced design and analysis of computational infrastructures for data-intensive applications.

Data Analytics Concentration

● Advanced modeling and application of statistical and machine learning methods across industries.