Computer Science - Junior

Course # COMP 3023

Credits 6

Pre-requisites and Co-requisites: Programming II

Course Description

The Operating Systems and System Programming introduces the student to the server operating systems. Students learn the basics of installing, administering, and maintaining the Ubuntu Server on virtual machines and optionally on Raspberry Pi 400 / 4B IoT boards. Administration of user and group accounts, SSH, DHCP, DNS, Apache, MariaDB, MPICH, backup, recovery, and disaster planning are covered. The Windows OS administration, Active Directory, and PowerShell are briefly discussed. Students are guided through laboratory assignments designed to give them practical real-world experience. Students are encouraged to design, implement, and evaluate small-scale projects in teams of up to three people.

Course Learning Outcomes

Upon the completion of the course, students will be able to:

• Use multiple computer system platforms and understand the advantages of each
• Install and administer Ubuntu server on virtual machines and optionally on Raspberry Pi 400 / 4B IoT boards
• Utilize SSH and related tools to remotely manage servers and infrastructure and secure users' information on computer systems
• Use the command line interface for Ubuntu server administration
• Install and configure Windows Server as a domain controller
• Install and manage disks, file systems, Docker, and web/email/DNS servers
• Demonstrate strategies for planning/designing/recovering server systems
• Course Assessments and Grading

Course # COMP 3052

Credits 6

Pre-requisites and Co-requisites: Object-Oriented Programming, Web Technologies

Course Description

This course explores the principles and practices of professional software development. Students will learn about software life cycle models, requirements engineering, system design, testing, and maintenance. The course combines theory with hands-on practice and is divided into two modules, with a midterm exam in between. Topics include Agile methods, UML modeling, design patterns, Git version control, testing strategies, and software project management. By the end of the course, students will be able to design and develop quality software systems ready for real-world use.

Course Learning Outcomes

Upon the completion of the course, students will be able to:

• Apply software development life cycle models to plan and manage software projects effectively.
• Elicit, document, and analyze software requirements using structured techniques and modeling tools like UML.
• Design modular, maintainable software systems following established design principles and patterns.
• Implement, test, and version control software applications using industry-standard tools and practices.
• Collaborate in teams using Agile methodologies to deliver working software increments and reflect on the development process.

Course Assessments and Grading

Course # COMP 3073

Credits 6

Pre-requisites and Co-requisites: Probability and statistics, linear algebra, a programing language

Course Description

According to Tom Mitchell “The field of Machine Learning is concerned with the question of how to construct computer programmes that automatically improve with experience”. This course covers the basic concepts and techniques of Machine Learning from both theoretical and practical perspective. The material includes classical machine learning approaches such as Linear Regression and Decision Trees, more advanced approaches such as recurrent neural network and convolution neural network, etc. The course explains how to build systems that learn and adapt using examples from real-world applications.

Course Learning Outcomes

Upon completion of the course, the students should be able to:

• Explain different machine learning techniques and select appropriate learning technique to solve a problem.
• Examine the computation complexity of different machine learning algorithms.
• Analyze machine learning algorithms using different performance evaluation metrices.
• Apply the machine learning algorithms to real-world
• Implement machine learning algorithms using computer programing languages.

Course Assessments and Grading

Course # DMNS 3032E

Credits 6

Pre-requisites and Co-requisites: Introduction to Probability and Statistics; Calculus-I

Course Description

This course introduces advanced topics in statistics for computer science majors. This course teaches essential background and techniques for understanding advanced statistical methods, enabling students to perform data analysis and evaluate research. The course starts with a review of introductory statistics and probability, then covers topics such as sampling distributions, point estimation, inference, ANOVA, and an introduction to machine learning. Python and/or R programming package will be used to enhance understanding and application of statistical techniques taught throughout the course.

Course Learning Outcomes

Upon completion of the course, students should be able to:

• Define sampling distribution and its properties.
• Test statistical hypotheses and determine significance.
• Analyze data using programming language and interpret results.
• Select appropriate statistical models and justify choice.
• Regress data using programming language and interpret results.
• Predict and draw conclusions using linear and multiple regression.
• Analyze data using programming packages.

Course Assessments and Grading

Course # MDIA 2127 

Credits 3

Pre-requisites and Co-requisites: None

Course Description

This course is designed to provide students with a grounding in graphic design. It introduces the intricacies of graphic genres and focuses on the importance of design-thinking in various media professions. The goal of the course is to enable students to enhance their creative thinking and visual ideation. The course also includes technical skills in relation to visual design computer programs through practical workshops. The course will also introduce students to a range of design spheres such as motion picture, TV captions, animation, 3D and web design.

Course Learning Outcomes

Upon completion of the course, students should be able to:

• Explain the visual design narratives used in a variety of media production
• Apply graphic design principles in the ideation, development, and production of visual
• Create visual design products for diverse communication contexts and
• Analyse genres, styles and trends in the history of visual
• Discuss the dark forces of design and the impact they can have

Course Assessments and Grading

Course #COMP 1073

Credits 6

Pre-requisites and Co-requisites: None

Course Description

This "Introduction to Computer Science" course is designed for students majoring in Communications and Media, offering a comprehensive overview computational literacy and the fundamental concepts in computer science with a special focus on Data Science and AI. Throughout the semester, students will explore key areas including data analytics, artificial intelligence, machine learning, digital media, human centered computing, user experience, social media platforms, search engine optimization, information security, and privacy. The course emphasises practical skills in data analysis, visualization, and human centered computing, culminating in a final project where students will apply their knowledge to real-world projects. Ethical considerations in research, data science, and AI are also integral to the curriculum, preparing students for responsible and informed engagement in the digital world.

Course Learning Outcomes

Upon completion of the course, students should be able to:

• Explain fundamental computing concepts and how computer systems, data, and networks support modern digital communication and media platforms.
• Apply basic data science and AI concepts to analyse, interpret, and communicate insights from digital media and social data.
• Describe how machine learning and recommendation algorithms operate and critically evaluate their impact on content visibility, public opinion, and audience behaviour.
• Design and evaluate simple web-based and interactive media content using principles of user experience, accessibility, and human centred computing.
• Demonstrate awareness of digital security, privacy, and ethical risks in media technologies, and propose responsible and lawful solutions.
• Create data driven or interactive digital stories using cloud based and web technologies, showing effective communication, creativity, and social responsibility.

Course Assessments and Grading

Course # ECON 4010E

Credits 6

Pre-requisites and Co-requisites: None

Course Description

This course uses a project-based learning approach to help students providing useful applications and concrete contributions in support of local development. For Communication and Media students the focus will be on audio and video production. Students will work in teams (of four or five students) to integrate music, graphics and video technologies in entrepreneurial projects aimed at supporting the local communities. For Computer Sciences students, a variety of mobile applications, augmented virtual reality applications, Big Data applications, Internet of Things, Video Game Experiential Marketing applications, Machine Learning Methods, Mobile Operating Systems and Mobile Signals and Sensors applications and many more will be on offer. Whenever possible, multidisciplinary collaborations between students will be suggested and recommended. The aim is to boost local development, preferably in the Naryn Oblast.

The student projects can be implemented in a variety of sectors such as tourism, agriculture, food processing, manufacturing, hospitality services (sports, leisure & recreation), public services, health, education, transportation, or any sector that contributes to support the development of local communities. However, an emphasis will be put on the tourism sector which has the potential to substantially contribute to Naryn’s economic progress.

Course Learning Outcomes

Upon completion the course, students should be able to:

• Define what are local development priorities and strategies
• Explain how specific digital projects can contribute to these goals
• Collect relevant data on which to build a digital project
• Determine the needs expressed by actors on the ground and design potential solutions to address those needs
• Relate their theoretical knowledge to the design and implementation of concrete projects on the ground
• Develop appropriate technical solutions to serve the specific needs of economic and social actors in the region
• Present to the public at large specific finalized projects

Course Assessments and Grading

Course # COOP 3001

Credits 2

Course # HUSS 3082

Credits 0

Pre-requisites and Co-requisites: None

Course description

The purpose of physical education is to strengthen health and develop the physical and mental abilities of students. Physical exercises and sports games are the way to a powerful and functional body, clear mind and strong spirit. The course is both practical and theoretical, it covers basic concepts of anatomy and physiology as well as health and safety requirements.  

Course learning outcomes

Upon completion of the course, students should be able to: 

• Perform a range of physical activities
• Understand health and safety requirements for a range of physical activities
• Describe the role and progress of sport in Central Asia
• Choose an appropriate physical activities programme for their age and gender
• Identify tiredness and its symptoms to control the body during athletic exercises
• Describe the technique of running for a long and a short distance and jumping
• Accomplish running for a short and a long distance and jumping according to all necessary norms
• Describe the rules of a range of sports games

Course Assessments and Grading

The course will be graded with PASS/FAIL.