The Master’s program in Computer Science aims to qualify highly skilled specialists with deep knowledge in modern computer science fields, focusing on artificial intelligence, natural language processing, data science, and web sciences. The program offers an integrated curriculum combining theoretical and practical aspects, promoting scientific research and innovation to meet the labor market’s evolving demands. Students can specialize in different tracks that provide advanced skills in specialized fields, contributing to the development of complex technical solutions, big data analysis, and advanced web applications. The program prepares graduates to engage in advanced work environments, participate in research and applied projects, and excel professionally in academic and practical sectors. It also fosters innovation and continuous learning to keep pace with rapid technological changes in computer science.

Vision

To be a leading local and global master’s program in education and research in computer science, contributing to the development of modern technologies that support digital and community development.

Mission

To provide a distinguished educational and research environment that develops students’ skills in computer science, with a focus on modern specializations, encouraging innovation and critical thinking to prepare graduates capable of facing digital age challenges and contributing to knowledge and technology advancement.

Objectives

  1. Deepen theoretical and practical knowledge in modern computer science fields.

  2. Develop scientific research and critical analysis skills.

  3. Qualify students to design and develop innovative technical solutions.

  4. Enhance capabilities in artificial intelligence, natural language processing, data science, and web sciences.

  5. Stimulate continuous learning and innovation in computer science.

  6. Prepare distinguished graduates capable of competing in the labor market and academic fields.

Learning Outcomes

Upon program completion, students will be able to:

  1. Understand and apply fundamental and advanced computer science concepts.

  2. Conduct specialized scientific research using rigorous methodologies.

  3. Develop advanced technical solutions in AI, NLP, data science, and web sciences.

  4. Analyze big data and apply machine learning and intelligent models.

  5. Design and develop advanced web applications meeting institutional needs.

  6. Communicate effectively and professionally within multidisciplinary teams.

  7. Keep up with technological advancements and engage in lifelong learning.

General Track

The General Track in Computer Science represents the ideal choice for students who wish to build a solid and comprehensive knowledge foundation in the field of computer science, with a focus on fundamental theoretical and practical aspects. The track offers a balanced set of courses covering key topics such as algorithms, data structures, operating systems, databases, and networks, preparing students for a thorough understanding of the essential concepts that form the core of computer science.

This track also enhances critical thinking and problem-solving skills, providing advanced programming tools and techniques necessary for software and application development in diverse environments. It aims to prepare graduates who are capable of addressing the challenges posed by evolving technologies and developing innovative technical solutions that meet the needs of both the market and research domains.

Furthermore, the track includes training in research methodology, qualifying students to contribute to research and innovative projects within universities or industrial institutions. Thanks to this balance between theoretical and practical knowledge, the track empowers its graduates to pursue advanced studies or to enter the labor market as qualified professionals in information technology and computer science fields.

Artificial Intelligence and Natural Language Processing Track

This track focuses on studying and developing advanced artificial intelligence technologies, with special emphasis on natural language processing (NLP) that enables computers to understand and analyze human language. Students learn models and algorithms used in deep learning, neural networks, text analysis, machine translation, and human-computer interaction. The track aims to prepare graduates capable of designing and developing intelligent systems used in various fields such as chatbots, sentiment analysis, and recommendation systems. It also emphasizes scientific research and innovation in AI and its practical applications, enabling graduates to compete in advanced technological markets.

Data Science Track

This track focuses on big data analysis and knowledge extraction using advanced techniques in data mining, machine learning, and statistics. Students gain skills in processing large datasets, building predictive models, and analyzing trends and patterns to extract insights that support strategic decision-making in organizations. The track includes practical training in data science tools and techniques such as Python programming, data analysis, and data visualization. It aims to prepare specialists capable of tackling big data challenges and employing them to improve operations and services across various sectors.

Web Sciences Track

This track focuses on the scientific and technical aspects of web development, including building and designing advanced web applications, database management, and web security. Students learn modern web programming technologies, network protocols, and integrated system design over the internet. The track also covers research topics in web sciences such as online data analysis, search engine optimization, and user-system interaction. It aims to prepare graduates capable of developing and maintaining advanced web systems that meet modern digital institutions’ needs, emphasizing performance, security, and usability.

Study Duration

● The student studies eight courses, distributed as follows:
√ Six compulsory courses.
√ Two elective courses from among the courses offered by the college for master’s students.
● The study is conducted through research seminars in each course. The research seminar is based on multiple references and is in accordance with the methodology and standards of scientific research.
● The study of each of the eight courses takes four credit hours for a minimum of four weeks and may exceed that according to each student’s abilities. After that, the student’s competency and knowledge test is held in the course he completed, then he begins another course in the same manner, and so on.
● The student is assigned a hypothetical course that the college chooses from among the courses that the student has studied at the undergraduate level, and this is considered a practical training for the student to be evaluated with ten credit hours. The student must divide this course from twelve to fourteen brief lectures. The student presents each lecture in the form of a written summary of its topic in Word or PDF format, accompanied by a video recording of it in the student’s voice using the Power Point program, with a duration of not less than ten minutes and not more than About twenty minutes.
● Study courses in the first year, the student has the right to extend the study for a period not exceeding a second year.

The requirements for obtaining a master’s degree in various disciplines are sixty credit hours according to the study plan approved by the University Council, and these requirements are distributed according to the following programs:
1- Research courses of thirty-two credit hours.
2- A scientific thesis with eighteen credit hours.
3- A practical training of ten credit hours.

Requirements for registering a thesis topic for a master’s degree
● The student must pass the stipulated courses with a score of at least 70%.
● The student obtained a TOEFL certificate of at least 450, or its equivalent, or obtained an equivalent certificate in the French language, with the exception of those who obtained a first university degree in one of the two languages, or in one of the two languages.
● The student submits a request to the university administration to register a master’s thesis with a proposed topic in one of the subspecialty tracks.
● If the initial approval of the subject title is achieved, the college council will designate a supervisor to guide and follow up the student in preparing the plan.
● The research plan includes the importance of the topic and a critical presentation of previous studies in it, specifically the research problem, then defining the study’s curriculum and its main hypotheses or questions that you want to answer, and the division of the study and its sources.
● The student presents his proposed plan in a scientific seminar, discussing the plan as a topic and an approach.
● The student adjusts his plan based on the professors’ observations in the seminar if he is asked to amend.
● After the seminar, the plan is presented to the college council to take its decision regarding the registration of the subject.
● In the event of approval, the College Council’s decision is presented to the University Council to approve the registration, and the registration date is calculated from the date of the University Council’s approval.

Jury discussion and award of degree
● The minimum for preparing a master’s thesis is nine months, starting from the date of approval by the University Council to register the subject, and the maximum is two years, which can be extended for a third exceptional year upon the recommendation of the supervisor and the approval of the College Council, provided that the total period of student enrollment in the degree does not exceed four years.
● The supervisor submits a semi-annual report that includes what has been accomplished and what is required in the remaining period.
● After the student completes the thesis and the supervisor reviews it, the supervisor submits to the university administration a report stating its validity for discussion, including an evaluation of the student’s performance during the preparation period of the thesis of 140 degrees, with a full copy of the thesis signed by him, and a letter with the names of the discussion and judgment committee proposed by the professors of the specialty, for presentation to the college Council.
● At least fifteen days must pass before the student’s discussion from the date of the approval of the discussion committee by the college.
● The validity period of the committee formed to discuss the thesis is six months, which may be renewed for a similar period based on a report from the supervisor and the approval of the College Council.
● Each member of the committee writes a detailed scientific report on the validity of the thesis for discussion, and the thesis is evaluated out of 420 degrees, and the average of the three degrees is taken.
The student may not be discussed unless he/she gets at least 70% of the supervisor’s evaluation of his performance and the committee members’ evaluation of the message in the individual reports.
● A group report is submitted after the discussion, signed by all members of the committee, in which an evaluation of the thesis discussion is given on a scale of 140 degrees.

The thesis is passed after public discussion with one of the ratings shown in the following table:

Percentage of grades points appreciation symbol Appreciation
Arabic Einglish
95 to 100% 4 A+ A+ Prominent
90 to less than 95% 7 , 3 a A
85 to less than 90% 3 , 3 b+ B Very well
80 to less than 85% 3 B B
75 to less than 80% 7 , 2 c+ C+ Good
70 to less than 75% 3 , 2 c C

After the college approves the student’s results, the master’s degree is awarded at a rate calculated from the average total of the courses and thesis grades.
After obtaining the approval of the University Council to grant a master’s degree to the student, he is entitled to obtain insured certificates, authenticated by the university, stating that he obtained that degree, in order to present them to the various authorities.

Study Duration

The duration of study to obtain a master’s degree in Political and economics is two years as a minimum, and six years as a maximum.
In the first year, the student studies at least eight subjects, and the study is through research seminars for each course. The research seminar is based on multiple references and is in accordance with the scientific research methodology and standards.
In the second year, the student attends a general seminar for the topic of the thesis, which he will prepare and submit for discussion
The general seminar is discussed by the scientific committee at the university, and the title of the thesis is approved
The student works to complete his thesis under the supervision of the supervisor decided by the Presidency of the University based on the proposal of the Dean of the Faculty
The student completes his scientific thesis and submits for discussion before the committee formed by the Presidency of the University in a public session and completes the conditions for a master’s degree
Courses of study in the first year The student has the right to extend the study in it for a period not exceeding a second year
The thesis prepared by the student during a period of time not less than 9 months and not exceeding two years

Conditions for success and graduation

1) The student is considered to have passed any of the program’s courses if he achieves a final score of no less than 65%. He is also considered successful in the master’s project if he obtains a mark (granted by the judging committee) not less than 75%.
After the student presents the results of his project before the committee, and discusses its technical content.
2) The student is not entitled to submit to discuss his thesis until a scientific research is published in an approved refereed journal.
3) The student obtains a master’s degree certificate after he has fulfilled all the scientific requirements for this degree.

Academic Track Structure
8 courses = 32 credit hours,
 practical training = 10 credit hours
 Master's thesis = 18 credit hours
Courses
Practical Training
Master's Thesis

Study Plan for General Track in Computer Science

Course Code Course Title Credit Hours Course Description
CS501 Research Methodology 4 This course introduces the principles and methodologies of scientific research in computer science, covering problem formulation, literature review, research design, data analysis, and report writing. It equips students with skills to conduct organized and rigorous research contributing to the advancement of knowledge in the field. Topics include data collection techniques, qualitative and quantitative analysis tools, and research ethics. By the end, students will be capable of preparing research proposals, scientific publications, and participating in research conferences.
CS502 Advanced Algorithms 4 Focuses on the design and analysis of advanced algorithms used in computer science, including divide-and-conquer techniques, dynamic programming, randomized algorithms, and complexity analysis. Enables students to develop efficient solutions to complex computational problems, blending theory with practical programming exercises. Upon completion, students will be able to design high-efficiency algorithms and evaluate their performance in various environments.
CS503 Operating Systems 4 Covers fundamental principles of operating systems such as process management, memory management, file systems, and scheduling. Students learn the design and operation of various OS types and study security and protection mechanisms. Practical components include working with real operating systems like Linux and Windows. Students will be able to analyze OS performance and develop programs that interact efficiently with operating systems.
CS504 Data Structures 4 Studies various data structures like lists, trees, hash tables, and graphs, focusing on data representation and organization to facilitate efficient searching, insertion, and deletion. Includes design and implementation using programming languages and performance analysis. Encourages problem-solving through appropriate data structure selection and efficiency evaluation.
CS505 Database Systems 4 Introduces database systems fundamentals, database design, SQL query language, and data management. Focuses on data modeling, database creation, integrity, and security. Includes relational and non-relational database management systems. Students learn to design integrated databases, optimize queries, and perform maintenance. By course end, students can design and implement databases supporting various software applications and organizational needs.

Elective Courses

Course Code Course Title Credit Hours Course Description
CS511 Artificial Intelligence 4 Covers fundamentals of AI including expert systems, machine learning, fuzzy logic, and neural networks. Enables students to understand and design intelligent problem-solving systems. Includes case studies and practical applications.
CS512 Machine Learning 4 Focuses on supervised, unsupervised, and reinforcement learning algorithms and models. Includes practical applications in data analysis and predictive modeling. Students will be able to apply ML tools to solve complex problems.
CS513 Cloud Computing 4 Introduces cloud computing concepts, cloud system architectures, data storage, and resource management. Prepares students to design and deploy scalable cloud services. Includes hands-on experience with platforms like AWS and Azure.
CS514 Information Security 4 Covers information security concepts, encryption, cyber-attack defenses, and security systems. Students learn risk management and tools for network and data protection, with practical applications. Graduates can analyze vulnerabilities and secure technical environments.
CS515 Advanced Programming 4 Deepens programming skills with advanced languages and techniques, emphasizing complex software design, design patterns, and performance optimization. Includes applied projects to build sophisticated software.
CS516 Computer Networks 4 Studies principles of computer networks, network layers, protocols, and network security. Includes local and wide-area network design and management with practical experience.
CS517 Web Application Programming 4 Focuses on developing web applications using modern languages and frameworks such as HTML, CSS, JavaScript, and others. Covers UI design, database integration, and web application security.
CS518 Distributed Computing 4 Covers distributed computing concepts, multi-node systems, synchronization, and data management in distributed environments. Prepares students to design and implement scalable distributed systems.
CS519 Systems Analysis and Design 4 Introduces methodologies for analyzing and designing information systems, process modeling, and software project management. Equips students to develop efficient information systems meeting organizational needs.
CS520 Scientific Programming 4 Focuses on programming applications in scientific and engineering fields using languages like MATLAB and Python to solve scientific problems.

Study Plan for Artificial Intelligence and Natural Language Processing Track

Course Code Course Title Credit Hours Course Description
AI601 Principles of Artificial Intelligence 4 Introduces AI fundamentals, concepts, and models such as logical reasoning, knowledge representation, and expert systems. Enables students to build intelligent systems.
AI602 Machine Learning 4 Focuses on machine learning algorithms including supervised, unsupervised, and deep learning, with practical applications in data analysis and intelligent model building.
AI603 Natural Language Processing 4 Studies techniques for processing human language including text analysis, speech recognition, machine translation, and information extraction. Enhances ability to design interactive intelligent systems.
AI604 Neural Networks and Deep Learning 4 Covers design and analysis of artificial neural networks, deep learning techniques, and applications with practical exercises.
AI605 Research Methodology in Artificial Intelligence 4 Develops research skills specialized in AI, from problem formulation to analysis and report writing.

Elective Courses

Course Code Course Title Credit Hours Course Description
AI611 Reinforcement Learning 4 Studies reinforcement learning techniques to develop intelligent systems that learn from environmental interactions.
AI612 Computer Vision 4 Principles and techniques for processing images and video to interpret and analyze visual content.
AI613 Recommender Systems 4 Design and development of intelligent recommendation systems using AI techniques.
AI614 Human-Computer Interaction 4 Study of methods and tools enabling effective interaction between users and intelligent systems.
AI615 Arabic Natural Language Processing 4 Advanced applications and techniques in processing the Arabic language including text analysis and speech recognition.
AI616 Sentiment Analysis 4 Techniques to detect and understand emotions and opinions in text using AI.
AI617 Python Programming for AI 4 Using Python to develop and implement various AI algorithms.
AI618 Expert Systems 4 Study and design of systems capable of providing expert advice and solutions in specialized fields.
AI619 Cloud Computing for AI 4 Utilizing cloud computing to develop and operate large-scale AI systems.
AI620 Information Extraction Techniques 4 Techniques for extracting important information from large and complex textual data.

Study Plan for Web Sciences Track

Course Code Course Title Credit Hours Course Description
WEB601 Web Development Technologies 4 Study of foundational and advanced web development techniques using HTML, CSS, JavaScript, and modern frameworks.
WEB602 Web Database Systems 4 Design and management of databases used in web applications, focusing on SQL and relational and non-relational databases.
WEB603 Web Application Security 4 Analysis and securing of web applications against cybersecurity threats using various protection techniques.
WEB604 Software Engineering for Web Applications 4 Principles of designing and developing large-scale web systems with emphasis on scalability and maintainability.
WEB605 Research Methodology in Web Sciences 4 Research skills specialized in web sciences, including proposal preparation and scientific writing in this field.

Elective Courses

Course Code Course Title Credit Hours Course Description
WEB611 Mobile Application Development 4 Design and development of applications for smart devices such as Android and iOS.
WEB612 Server-Side Programming 4 Development of server-side software to support web applications using languages like PHP, Node.js, Python.
WEB613 Web Data Analytics 4 Study and analysis of user activity data online to extract useful insights.
WEB614 Search Engine Optimization (SEO) 4 Strategies and methods to improve website visibility in search engines for increased traffic and effectiveness.
WEB615 User Interface and User Experience Design 4 Designing interactive and user-friendly interfaces to improve digital application user experiences.
WEB616 Cloud Computing for Web Applications 4 Leveraging cloud technologies to provide flexible hosting environments for web applications.
WEB617 Content Management Systems and Information Management 4 Study of content management systems and electronic documentation systems in different web environments.
WEB618 Modern Web Technologies 4 Overview of the latest web development technologies like WebAssembly and Progressive Web Apps (PWAs).
WEB619 Web Network Security 4 Analysis and application of network security techniques to protect web networks from cyber attacks.
WEB620 Web Services and APIs 4 Design and development of web services and integration of systems using APIs.

Study Plan for Data Science Track

Course Code Course Title Credit Hours Course Description
DS601 Introduction to Data Science 4 Overview of data science principles, stages, and major tools and techniques used to analyze data and extract knowledge.
DS602 Statistical Data Analysis 4 Study of statistical methods for data analysis and interpretation using analytical software tools.
DS603 Machine Learning for Data Science 4 Application of machine learning techniques for big data analysis and predictive modeling.
DS604 Big Data Database Management 4 Design and management of large databases, storage solutions, and handling of big data applications.
DS605 Research Methodology in Data Science 4 Research skills focusing on data science, study design, data analysis, and publication of findings.

Elective Courses

Course Code Course Title Credit Hours Course Description
DS611 Data Mining 4 Techniques and methods for discovering patterns and knowledge from large and complex datasets.
DS612 Data Visualization 4 Using tools and techniques to graphically represent data for effective analysis and communication.
DS613 Python Programming for Data Analysis 4 Teaching Python for data analysis with libraries such as Pandas, NumPy, and Matplotlib.
DS614 Cloud Computing in Data Science 4 Utilizing cloud computing to efficiently process and analyze big data.
DS615 Predictive Modeling 4 Building and evaluating predictive models using machine learning and statistical techniques.
DS616 Unstructured Data Analysis 4 Analyzing text, image, and video data using unstructured data processing techniques.

Welcome to the Institute of Postgraduate Studies at ISU