Launch of the Sixth Semester of the Bachelor of Psychology Program – Second Cohort
Bachelor in Computer Science and Artificial Intelligence
Bachelor in Computer Science and Artificial Intelligence
UHA University is a U.S.-based institution accredited by the International Education Quality Management (IEQM) in the United States of America.
The university has been listed in the Impact Rankings 2025 issued by the global Times Higher Education organization.
About the Program
The Bachelor’s in Computer Science and Artificial Intelligence is designed to provide students with a strong foundation in computing, programming, and AI
This program combines theoretical knowledge with practical applications to equip students with the necessary skills to excel in the rapidly evolving fields of computer science and artificial intelligence
Students will learn fundamental programming concepts, data structures, machine learning, and AI-driven decision-making while gaining hands-on experience through projects and practical training
Program Details
The program consists of 120 credit hours spread over 8 semesters in two and a half years
It covers essential topics such as computer programming, data science, artificial intelligence, and natural language processing
The curriculum also includes a wide range of elective courses, allowing students to specialize in areas such as digital image processing, data mining, and information retrieval
Study Plan (by Semesters):
First Semester
|
# |
Course Name | Credit Hours | Suggested Code | Description |
| 1 | Introduction to Computer Science | 3 | CS 1 | This course introduces the fundamental concepts of computer science, including computer components, operating systems, and networks. It also covers how software and hardware work, and the basics of problem-solving using algorithms. Students are also introduced to fields of computer science such as artificial intelligence, databases, and cybersecurity. |
| 2 | Computer Programming 1 | 3 | PROG 1 | This course aims to teach the basics of programming using a programming language such as Python or Java. Students learn to write programs, control data flow using conditional statements and loops, and handle arrays and functions. Emphasis is also placed on problem-solving skills and developing students' logical thinking. |
| 3 | Academic English 1 | 3 | EN 1 | This course focuses on developing students' academic English skills, such as reading, writing, and comprehension. The course covers writing academic essays, summarizing texts, and information research skills. It also includes improving speaking and listening skills through interactive activities. |
| 4 | Mathematics 1 | 3 | MATH 1 | This course aims to provide first-year students with the fundamentals of mathematics, covering the basic concepts of sets, real numbers, and functions. Students learn to analyze the properties of various types of functions, including radical, exponential, logarithmic, and trigonometric functions. Emphasis is placed on understanding limits and continuity and applying differentiation rules to analyze changes in functions. The course also includes the study of derivatives and their use in solving maximum and minimum problems, and understanding the geometric behavior of functions. The course also includes the study of concavity, inflection points, asymptotes, and accurate graphing of functions. Students are trained to resolve indeterminate forms using theorems such as L'Hôpital's rule. The course aims to develop students' logical and mathematical thinking and prepare them for studying advanced mathematics courses and applying mathematics to solve applied problems. |
| 5 | Introduction to Information Systems | 3 | IS 1 | This course introduces students to the fundamental concepts of information systems and their role in supporting operations and decision-making within institutions. It covers the components of information systems such as hardware, software, data, and procedures, in addition to types of information systems such as transaction processing systems and decision support systems. The course also reviews how information systems are used to improve work efficiency and data management in modern institutions. |
Second Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Computer Programming 2 | 3 | PROG 2 | This course focuses on advanced concepts in programming by deepening the skills acquired in Computer Programming 1. It includes topics such as object-oriented programming (OOP), file processing, exceptions, and basic data structures such as lists, arrays, and stacks. Students also learn how to build integrated programming projects and develop effective applications using the appropriate programming language. |
| 2 | Mathematics 2 | 3 | MATH 2 | The course covers applications of definite integration, including calculating areas and volumes using advanced methods. It also covers the study of arc lengths and surface areas in polar systems, with practical applications. The course focuses on the analysis of sequences and series, with an understanding of convergence tests. The study of functions of several variables is expanded, including limits and partial derivatives. It also addresses double integrals and transformations of regions, employing them in solving various mathematical and engineering problems. |
| 3 | Academic English 2 | 3 | EN 2 | This course continues developing academic English skills, with a focus on advanced writing and critical analysis. It includes writing reports and academic research, improving presentation skills, and expanding reading and comprehension abilities. Speaking skills are also enhanced through discussions and interactive presentations. |
| 4 | Digital Systems and Logic Design | 3 | LD 1 | The course covers the fundamental concepts of digital systems and logic circuit design. It includes the study of the binary system, Boolean algebra, simplification of logic circuits using Karnaugh maps, and the design of combinational and sequential circuits. Students are also introduced to basic processing units such as registers and timers, with practical application using digital system design tools and software. |
| 5 | Introduction to Entrepreneurship | 3 | BUS 1 | This course introduces students to the fundamental concepts of entrepreneurship and how to transform ideas into successful projects. It includes topics such as developing business plans, market research, innovation in products and services, and managing financial resources. The course also covers leadership skills, problem-solving, and decision-making, encouraging students to think entrepreneurially through practical case studies. |
Third Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Algorithms and Data Structures 1 | 3 | DSA 1 | This course aims to introduce students to the fundamental concepts of algorithms and data structures. It covers key topics such as linked lists, stacks, queues, binary trees, and search and sorting algorithms. The course focuses on analyzing algorithm complexity using concepts such as Big-O, with practical applications for building efficient solutions to programming problems. |
| 2 | Discrete Mathematics | 3 | MATH 3 | This course focuses on the fundamental mathematical concepts in computer science, such as sets, relations and functions, mathematical logic, combinatorics (permutations and combinations), and graph theory. It helps students develop logical thinking and problem-solving skills using the mathematical tools necessary in programming and algorithms. |
| 3 | Statistics and Probability 1 | 3 | STAT 1 | This course presents the fundamental principles of probability theory, including random variables, probability distributions (discrete and continuous), and basic laws such as the law of conditional probability. Emphasis is placed on practical applications in data analysis and decision-making, linking concepts to modern fields such as data science and artificial intelligence. |
| 4 | Object-Oriented Programming | 3 | PROG 3 | This course covers advanced concepts of object-oriented programming using languages such as Java or Python. It focuses on fundamental concepts such as objects, classes, inheritance, polymorphism, and abstraction. The course includes practical projects to build effective and reusable applications. |
| 5 | Data Science | 3 | DS 1 | This course aims to introduce students to the fundamentals of data science, including data collection, cleaning, and analysis using specialized tools and software such as Python and R. The course covers data visualization methods, statistical modeling, and the application of machine learning techniques to solve practical problems. It also addresses applied examples in various scientific and commercial fields. |
Fourth Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Algorithms and Data Structures 2 | 3 | DSA 2 | This course continues the study of advanced concepts in data structures and algorithms. It covers topics such as advanced trees (AVL Trees, B-Trees), graphs, shortest path algorithms such as Dijkstra and Floyd, and dynamic programming techniques. It focuses on performance optimization through algorithm complexity analysis and applying them to solve complex problems. |
| 2 | Principles of Artificial Intelligence | 3 | AI 1 | This course aims to introduce students to the fundamentals of artificial intelligence such as search algorithms, knowledge representation, and inference. The course covers modern applications including expert systems, natural language processing, and image recognition. Emphasis is placed on building AI-based solutions and using specialized software tools. Students are also guided toward practical projects to solve real problems using AI techniques. |
| 3 | Statistics and Probability 2 | 3 | STAT 2 | This course focuses on the fundamental concepts of applied statistics. It covers topics such as measures of central tendency (mean, median, mode), measures of dispersion (standard deviation), hypothesis estimation, regression analysis, and probability distributions. Emphasis is placed on using statistics in data analysis and solving real-world problems. |
| 4 | Principles of Software Engineering | 3 | SE 1 | This course introduces students to the foundations and practices of software engineering, from the stages of requirements analysis and system design to testing and deployment. It addresses development models such as Waterfall and Agile, and software project management tools. The course focuses on improving software quality and writing flexible and maintainable programs. |
| 5 | Elective Course 1 | 3 | ELEC 1 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
Fifth Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Fundamentals of Machine Learning | 3 | ML 1 | This course focuses on the fundamental concepts of machine learning, including supervised learning (such as regression and decision trees) and unsupervised learning (such as clustering). It includes reinforcement learning and algorithms such as neural networks. Concepts are applied practically using Python and libraries such as Scikit-learn. Students are encouraged to build predictive models and analyze their performance using various evaluation metrics. |
| 2 | Database Systems | 3 | DB 1 | This course focuses on designing, managing, and querying databases using database management systems (DBMS) such as SQL and NoSQL. It covers the fundamentals of relational design, indexes, and security, with practical applications for creating advanced databases. |
| 3 | Web Design and Development | 3 | WEB 1 | This course covers the principles of designing and developing web applications. It includes learning basic languages such as HTML, CSS, and JavaScript for front-end design, in addition to the fundamentals of back-end programming using languages such as PHP or Python. It also addresses the use of databases in web applications and connecting them to servers. Emphasis is also placed on designing responsive websites compatible with different devices. |
| 4 | R and Python Programming | 3 | STAT 3 | This course reviews the fundamental techniques in statistical learning and predictive models. It covers topics such as linear and logistic regression, neural networks, classification methods, and the use of data estimation techniques. Emphasis is placed on applying these concepts practically in big data analysis using tools such as R or Python. It also includes forecasting applications in various fields such as business and science. |
| 5 | Elective Course 2 | 3 | ELEC 2 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
Sixith Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Formal Languages and Automata Theory | 3 | TOC 1 | This course aims to study the theoretical foundations of computing, including formal languages, types of automata such as finite automata, and Turing machines. It covers topics related to grammars, and recognition of regular and context-free languages. It helps students understand how to design compilers and analyze computability and solve theoretical problems. |
| 2 | Operating Systems | 3 | OS 1 | This course addresses the fundamental principles of operating systems such as process management, memory, file systems, and storage. It focuses on how the operating system works in organizing hardware resources and running applications effectively. Topics also include synchronization, scheduling, and error handling. Practical examples of systems such as Linux and Windows are presented to understand design and development. |
| 3 | Operations Research | 3 | OR 1 | This course focuses on the study of mathematical modeling and its applications in solving engineering and scientific problems. It addresses topics such as linear programming, dynamic programming, and decision analysis. Students learn how to use mathematical models to optimize performance and make optimal decisions in applied fields such as operations management and economics, with practical applications using specialized software such as MATLAB. |
| 4 | Deep Learning | 3 | DL 1 | This course focuses on deep learning, one of the most important areas of artificial intelligence that relies on multi-layer artificial neural networks. The course will cover the fundamentals of neural networks, designing multi-layer perceptrons (MLPs), convolutional neural networks (CNNs) for computer vision, and recurrent neural networks (RNNs) for processing sequential data. In addition, students will learn model optimization techniques such as optimization algorithms, transfer learning, and generative models. Students will be trained to use machine learning libraries such as TensorFlow and PyTorch to design and implement deep learning models. |
| 5 | Elective Course 3 | 3 | ELEC 3 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
Seventh Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Graduation Project 1 | 3 | GP 1 | This course aims to develop students' scientific research and practical application skills. Students choose a research problem or topic under the supervision of a professor, and work on analyzing the problem and designing appropriate solutions. The project includes multiple stages such as: data collection, requirements analysis, preliminary design, and testing solutions. Emphasis is placed on presenting preliminary results and presenting them in an organized report showing the progress achieved in the project. |
| 2 | Computer Networks | 3 | NET 1 | This course covers the theoretical and practical foundations of computer networks, including the study of network layers according to the OSI and TCP/IP models. It addresses topics such as communication protocols, data routing, network security, and IP addressing. Students are introduced to modern networking technologies such as LAN and WAN and how to set up small networks. It includes practical training on network simulation tools and software such as Packet Tracer to understand the mechanism of data transfer and communication between devices. |
| 3 | Elective Course 4 | 3 | ELEC 4 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
| 4 | Elective Course 5 | 3 | ELEC 5 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
| 5 | Prompt Engineering | 3 | AI 2 | This course focuses on developing effective prompt engineering skills to improve the interaction of intelligent systems, especially generative AI systems such as ChatGPT and large language models (LLMs). The course addresses how to write prompts accurately and clearly to improve the outputs of intelligent models, covering performance optimization methods such as step-by-step responses, using examples, and defining context. Students learn how to formulate prompts to achieve desired results, and apply prompt testing strategies to improve the accuracy and quality of outputs. The course also reviews the uses of prompt engineering in applications such as natural language processing (NLP), text and image generation, and intelligent task automation. This course is essential for understanding how to harness modern AI technologies to achieve goals in various fields such as business, education, and scientific research. |
Eighth Semester
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Graduation Project 2 | 3 | GP 2 | This course is a continuation of Graduation Project 1, focusing on implementing the proposed solutions and evaluating them practically. The project stages include: developing the system or model, performance testing, results analysis, and documenting the final work. The project is presented in the form of a final report and a presentation showing the objectives, work methodology, results, and conclusions. This course aims to enhance students' research, critical thinking, and teamwork skills. |
| 2 | Network Security | 3 | NETSEC 1 | This course reviews the fundamental concepts of computer network security and the technologies used to protect data. It addresses topics such as encryption, firewalls, intrusion detection (IDS/IPS), and security protocols such as SSL/TLS and IPSec. The course focuses on methods of countering cyberattacks such as malware, denial-of-service (DDoS) attacks, and securing local and wide area networks. It includes practical training on security tools and network analysis to ensure protection from threats. |
| 3 | Elective Course 7 | 3 | ELEC 7 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
| 4 | Elective Course 8 | 3 | ELEC 8 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
| 5 | Elective Course 9 | 3 | ELEC 9 | This course allows students the opportunity to choose a specialized topic from among a range of modern topics. |
Elective courses are structured so that faculty teaching assignments can be easily managed; thus, only courses for which a professor is available are offered.
| # | Course Name | Credit Hours | Suggested Code | Description |
| 1 | Natural Language Processing | 3 | NLP 1 | This course aims to teach the theoretical and applied foundations of text and natural language processing. It covers fundamental topics such as text analysis, language models such as BERT, and entity recognition. Emphasis is placed on building applications such as machine translation systems, question answering, and sentiment analysis using Python libraries such as NLTK and spaCy. |
| 2 | Digital Image Processing | 3 | IP 1 | This course addresses the principles of digital image analysis and its applications. It includes topics such as enhancement, segmentation, image compression, and edge detection. The algorithms used such as Fourier Transform are studied. Practical training focuses on using OpenCV to apply image processing techniques. |
| 3 | Data Mining and Big Data | 3 | DM 1 | This course presents techniques for extracting knowledge from big data using modern tools such as Hadoop and Spark. It covers pattern mining algorithms, prediction, and big data analysis techniques in real applications. |
| 4 | GIS 1 | 3 | GIS 1 | This course presents the theoretical and applied foundations of geographic information systems, including spatial and descriptive data, vector and raster data models, coordinate systems, and spatial data management and analysis, with practical applications using GIS software. |
| 5 | Data Analysis | 3 | DA 1 | This course aims to equip students with data analysis and knowledge extraction skills using analytical tools. It includes topics such as statistics, regression analysis, and hypothesis testing. Software tools such as Python and R are used to analyze practical data and make data-driven decisions. |
| 6 | Soft Skills | 3 | SS 1 | This course aims to develop the personal and professional skills necessary for academic and professional success, including effective communication, teamwork, leadership, time management, problem-solving, critical thinking, negotiation, conflict management, presentation and delivery, and writing a CV and preparing for the job market. |
| 7 | GIS 2 | 3 | GIS 2 | This course addresses advanced topics in geographic information systems and spatial analysis, including network analysis, digital elevation models, remote sensing, multi-criteria analysis, and geographic modeling using ArcGIS. And electronic digital maps. |
| 8 | Reinforcement Learning | 3 | RL 1 | This course presents the concepts of reinforcement learning, where models learn through interaction with the environment and achieving rewards. Emphasis is placed on applications such as games and robot control using libraries such as OpenAI Gym and TensorFlow. |
| 9 | Introduction to Data Protection and Cybersecurity | 3 | Syp-sec | This course reviews the fundamental principles of data protection and information security. It covers topics such as encryption, risk management, personal data protection, and identifying security threats. Students learn to use basic tools to secure systems and networks against attacks. |
| 10 | Fundamentals of Internet of Things | 3 | IoT 1 | This course provides an introduction to Internet of Things (IoT) technologies and their basic architecture. It addresses topics such as smart devices, sensors, communication protocols, and data storage. Students are trained to build simple applications that connect devices to cloud networks for data analysis. |
| 11 | Cloud Systems and Networks | 3 | CNET 1 | This course reviews the architecture of cloud computing and the mechanism of managing cloud networks. It covers topics such as cloud services (IaaS, PaaS, SaaS), and network infrastructure. It focuses on tools such as AWS and Microsoft Azure and their applications in managing resources efficiently. |
| 12 | Cryptography | 3 | Crpt 1 | This course addresses mathematical methods and modern techniques in encryption to protect data. Emphasis is placed on algorithms such as RSA and AES and their application in protecting information and transmitting it securely. Students are trained to analyze and apply encryption mechanisms using specialized software. |
| 13 | AI in Business | 3 | AI-B 1 | This course focuses on applying artificial intelligence techniques in business and supporting decision-making, including machine learning, generative artificial intelligence, data analysis, intelligent automation and forecasting, and AI applications in various business functions. |
Key to Abbreviations Used:
- CS: Computer Science
- PROG: Programming
- MATH: Mathematics
- EN: English
- DSA: Data Structures & Algorithms
- STAT: Statistics
- AI: Artificial Intelligence
- ML: Machine Learning
- NET: Networks
- GP: Graduation Project
- ELEC: Elective Course
Program Benefits
This comprehensive program covers a broad spectrum of topics, from core computer science concepts to advanced AI applications
Students engage in hands-on projects, lab exercises, and field training to prepare for real-world challenges
The interdisciplinary nature of the program allows students to explore various fields such as natural language processing and data mining, ensuring a well-rounded education
Students gain experience with modern programming languages, AI frameworks, and industry-standard data analysis tools
The program also focuses on critical thinking, problem-solving, and teamwork, preparing graduates for competitive global roles
Accredited Certification
Study with us and earn an academic degree from UHA University, accredited by the International Education Quality Management (IEQM) in the United States of America.
Career Opportunities
Graduates of the Bachelor’s in Computer Science and Artificial Intelligence program will be well-prepared for various careers in technology and innovation, including
Artificial Intelligence Specialist
Software Developer
Data Scientist
Machine Learning Engineer
Information Security Analyst
Business Intelligence Analyst
Graduate Studies and Research
This program enables graduates to excel in dynamic and competitive job sectors
Required Admission Requirements
High school graduates from all branches
Tuition & Fees
The tuition fee for the bachelor’s program is $425 per semester
Number of semesters: 8, Total fees: $3400
College Registration Fee and Issuance of Acceptance Letter: $75 (one-time payment upon registration)
Discounts:
- A 15% discount is granted upon enrollment at the university when paying tuition fees in installments at the beginning of each semester.
- A 25% discount is granted when paying the full tuition fees in one payment after receiving the admission letter from the university.
How to Apply
Fill out the registration form or contact us
Contact via WhatsApp
Student Affairs Department:
905055566619+
12067047858+
Direct Contact with Admissions Coordinator: +16058469439
For more details, visit our website: www.uha.edu.eu
Contact via Email: info@uha.edu.eu
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