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Department of Data Engineering and Intelligent Systems

 

1. Vision, Mission, and Strategic Objectives

 

Vision:

To become a leading regional and international hub for education, research, and innovation in data engineering and intelligent systems, empowering graduates with advanced scientific knowledge and practical skills to shape the future of data-driven technologies and smart decision-making systems.

Mission:

The mission of the Department of Data Engineering and Intelligent Systems is to provide high-quality education that integrates theoretical foundations with practical, industry-oriented competencies in big data, cloud computing, analytics, and intelligent systems.

The department aims to cultivate professionals capable of designing, building, and managing large-scale data infrastructures and developing intelligent solutions that address real-world challenges across various sectors.

Strategic Objectives:

  • Provide a robust academic program grounded in engineering principles, data science, and intelligent technologies.
  • Equip students with practical skills in big data engineering, cloud platforms, machine learning, and intelligent decision-making systems.
  • Promote research and innovation in emerging areas such as data governance, autonomous intelligent systems, and advanced analytics.
  • Strengthen partnerships with industry to ensure curriculum alignment with market needs and to enhance student employability.
  • Prepare graduates for competitive careers in technology companies, research institutions, and data-driven organizations.
  • Encourage ethical and responsible use of data with an emphasis on privacy, fairness, and transparency.
  • Support continuous learning and professional development through seminars, certifications, and industry collaborations.
  • Develop leadership and teamwork skills enabling graduates to contribute effectively to interdisciplinary engineering environments.

2. Justification and Market Need Analysis

 

Introduction

The rapid digital transformation across industries has placed data at the center of innovation, economic growth, and strategic decision-making. As organizations generate massive volumes of structured and unstructured data, the need for professionals who can manage, analyze, and leverage this data through intelligent systems has become essential. Establishing the Department of Data Engineering and Intelligent Systems at Caluni University aligns with global technological trends and addresses an urgent market demand for advanced competencies in data-driven technologies.

Global Trends and Demand:

  • High Growth in Data Engineering Professions: According to international labor reports, roles such as Data Engineer, Machine Learning Engineer, Cloud Engineer, and AI Systems Architect are among the fastest-growing occupations worldwide.
  • Increasing Adoption of Big Data and Cloud Platforms: Enterprises are increasingly migrating to cloud ecosystems (e.g., AWS, Azure, Google Cloud) and adopting big data frameworks such as Hadoop, Spark, and Kafka, creating significant demand for trained specialists.
  • Rise of Intelligent Systems: Intelligent decision systems, automated analytics, recommendation engines, and autonomous systems have become fundamental across sectors including: healthcare, finance, telecommunications, energy, manufacturing, and transportation.

Regional and Local Market Needs:

  • Growing IT Sector in Türkiye and the region: Companies and institutions in Türkiye, Iraq, and the Middle East require skilled data specialists to develop and maintain digital infrastructures and innovative applications.
  • Industry Demand Exceeds Supply: Universities in the region traditionally offer programs in IT, Computer Science, or traditional engineering, but very few offer specialized programs in Data Engineering and Intelligent Systems, resulting in a talent gap in the job market.
  • Government and Private Sector Initiatives: Smart city projects, digital government platforms, Industry 4.0 transformation, and AI adoption in public and private sectors increase the demand for professionals with combined skills in data engineering and intelligent automation.

3. Academic Structure and Scientific Tracks

 

Overview

The Department of Data Engineering and Intelligent Systems is structured to provide students with both foundational and advanced competencies across data technologies and intelligent decision-making systems. The academic structure integrates engineering principles, computational methods, modern data architectures, and artificial intelligence concepts to equip graduates with the skills required in contemporary digital ecosystems.

The department is organized into four primary scientific tracks, each reflecting a major area of specialization in the field.

Scientific Tracks:

 

Track 1: Big Data Engineering and Data Infrastructure

Focus Areas

  • Large-scale data processing
  • Distributed computing architectures
  • Data pipelines and ETL systems
  • Data storage technologies (SQL, NoSQL, NewSQL)
  • Real-time and batch data processing frameworks (Spark, Hadoop, Kafka)

 

Student Outcomes

  • Ability to design and implement big data systems
  • Expertise in processing high-volume, high-velocity datasets
  • Hands-on experience with distributed data frameworks and data lifecycle management

Track 2: Cloud Computing and Distributed Systems

 

Focus Areas

  • Cloud platforms (AWS, Azure, Google Cloud)
  • Containerization and microservices (Docker, Kubernetes)
  • Serverless computing
  • Cloud security and data governance
  • High-availability and scalable architectures

Student Outcomes

  • Capability to architect, deploy, and manage cloud-native systems
  • Competence in secure and efficient application deployment
  • Skills aligning with market-recognized cloud certifications

Track 3: Intelligent Decision Systems and Machine Learning Engineering

 

Focus Areas

  • Machine learning and predictive analytics
  • Intelligent decision-making frameworks
  • Reinforcement learning and optimization
  • AI-driven automation
  • MLOps and deployment of intelligent models

 

Student Outcomes

  • Ability to build, train, evaluate, and deploy intelligent systems
  • Understanding of modern ML pipelines and MLOps workflows
  • Capability to integrate data engineering with AI for real-world decision systems

Track 4: Data Analytics, Visualization, and Business Intelligence

Focus Areas

  • Statistical modeling and data interpretation
  • Advanced analytics platforms (Power BI, Tableau)
  • Data storytelling and dashboard design
  • Business intelligence systems and KPIs
  • Applied analytics in finance, health, industry, and social systems

 

Student Outcomes

  • Ability to translate complex datasets into actionable insights
  • Strong analytical and visualization skills
  • Proficiency in BI tools and industry reporting techniques

These tracks are designed to complement each other and allow students to:

  • Build end-to-end data ecosystems
  • Integrate AI systems with data pipelines
  • Develop scalable and secure cloud-based solutions
  • Produce intelligent insights for strategic decision-making

 

The department encourages students to take courses across tracks to build interdisciplinary competence, preparing them for diverse roles in modern organizations.

Flexibility and Electives

  • Students may select electives within any track based on their career interests.
  • Capstone projects and internships can be completed in collaboration with industry partners to strengthen practical experience.
  • The department will periodically revise tracks to align with technological advancements and market needs.

 

 

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Sub Specializations

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Bachelor’s in Data Engineering and Intelligent Systems
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