Data Science and Artificial Intelligence
Program Overview
This comprehensive Master's program prepares students for careers in data science, machine learning, and artificial intelligence. The curriculum covers statistical analysis, deep learning, natural language processing, and computer vision. Students will work on real-world projects with industry partners and gain hands-on experience with cutting-edge tools and technologies. The program combines rigorous theoretical foundations with practical applications, ensuring graduates are well-equipped to tackle complex data-driven challenges in various industries. [TEST PROGRAM EDIT - April 13 2026 - Updated for workflow test]
Goals, Outcomes & Methods
To develop highly skilled data scientists and AI specialists who can drive innovation across industries. Graduates will possess strong analytical thinking, advanced technical skills in machine learning and deep learning, and the ability to translate complex data into actionable business insights. The program aims to foster critical thinking, ethical AI development practices, and interdisciplinary collaboration.rnrn[TEST PROGRAM EDIT - April 13 2026 - Updated tuition to reflect 2026 adjustments]
Upon completion, graduates will be able to:rn- Design and implement advanced machine learning modelsrn- Process and analyze large-scale datasets using modern toolsrn- Develop AI-powered applications for real-world problemsrn- Communicate technical findings to both technical and non-technical audiencesrn- Conduct independent research in data science or AIrn- Apply ethical frameworks to AI development and deploymentrn- Collaborate effectively in multidisciplinary teams
The program employs a blend of teaching methodologies including interactive lectures, hands-on laboratory sessions, case-based learning, industry guest lectures, flipped classroom approaches, and collaborative project work. Students benefit from a strong emphasis on practical application through capstone projects with industry partners, hackathons, and research seminars. Assessment methods include written examinations, programming assignments, research papers, project presentations, and portfolio reviews.
Program Structure & Plan
Year 1 - Semester 1:rn
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- Mathematical Foundations for Data Science
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- Introduction to Machine Learning
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- Statistical Methods & Probability Theory
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- Programming for Data Science (Python & R)
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- Database Systems & SQL
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rnYear 1 - Semester 2:rn
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- Deep Learning & Neural Networks
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- Natural Language Processing
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- Computer Vision
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- Data Engineering & Big Data Platforms
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- Research Methods & Ethics in AI
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rnYear 2 - Semester 1:rn
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- Advanced Machine Learning
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- Reinforcement Learning
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- AI in Healthcare & FinTech (Elective)
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- Industry Capstone Project Phase 1
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rnYear 2 - Semester 2:rn
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- Thesis Research & Writing
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- Industry Capstone Project Phase 2
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- Professional Development & Career Planning
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Tuition & Fees
| Fee Type | Year 1 | Year 2 | Total |
|---|---|---|---|
| Academic Fees | |||
|
Tuition Fee
|
9,200 | 9,200 | 18,400 |
|
Application Fee
One-time · non-refundable
|
100 | — | 100 |
| Grand Total | 9,300 | 9,200 | 18,500 |
Merit-Based Excellence Scholarship
— Up to 30% tuition waiver
Available for exceptional candidates with strong academic records, research potential, and demonstrated leadership. Applicants must have a minimum GPA of 3.5/4.0 and submit a personal statement outlining their contributions to the field of data science or AI.
Who Should Apply
This program is ideal for recent graduates with a background in STEM fields who want to specialize in data science or AI. It is also suitable for working professionals in IT, engineering, finance, or analytics who wish to upskill and transition into data science roles. Applicants should have a strong aptitude for mathematics and programming, along with a passion for solving complex problems using data-driven approaches.
Admission Requirements
Bachelor degree in Computer Science, Mathematics, Statistics, or related field with minimum GPA 3.0/4.0. Strong quantitative background required. Programming proficiency in Python or R is preferred. Relevant work experience in data analysis or software development is a plus but not mandatory.
Required Documents
Career Prospects
Data ScientistrnMachine Learning EngineerrnAI Research ScientistrnData EngineerrnBusiness Intelligence AnalystrnNLP EngineerrnComputer Vision EngineerrnAI Product ManagerrnQuantitative AnalystrnResearch ScientistrnDeep Learning SpecialistrnData Analytics Consultant
University Accreditation & Recognition
Program Accreditation & Recognition
Ministry of Education and Science of GeorgiarnEuropean Credit Transfer and Accumulation System (ECTS)rnBologna Process Compliant
General Information
The program is offered at Caucasus University's modern campus in Tbilisi, Georgia. Classes are held in state-of-the-art computer labs equipped with high-performance computing resources. Students have access to GPU clusters for deep learning projects, extensive digital libraries, and industry-standard software including TensorFlow, PyTorch, Jupyter, and Tableau. The university maintains partnerships with leading tech companies for internship and capstone project opportunities. International students receive support with visa processes, accommodation, and cultural integration.
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