The course covers the use of general purpose programming language to solve problems. The emphasis is to train students to design, implement, test, and debug programs intended to solve computing problems using fundamental programming constructs. By the end of the course, students are expected to demonstrate proficiency in Python programming by designing and developing a functional software application. This system must effectively apply core programming concepts discussed in the course, including problem analysis, algorithm design, control structures, data structures, and debugging techniques.

This course provides an overview of the Computing Industry and Computing of profession, including research and Applications in different fields; an Appreciation Computing in different fields such as Biology, Sociology, Environment and Gaming; an Understanding of ACM Requirements; an Appreciation of the history of computing; and Knowledge of the Key Components of Computer Systems (Organization and Architecture), Malware, Computer Security, Internet and Internet protocols, HTML5 and CSS. The student is expected to develop websites and web applications integrating the concepts of HTML/CSS.

This course covers matrices, matrix operations and properties and determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, and diagonalization.

This course begins with the basic concepts and definitions on mathematical models and applications of differential equations to real-world problems. The focus is to teach you how to model the world in terms of differential equations, and how to solve equations and interpret the solutions in computing. Topic includes First-order and higher-order differential equations, along with the methods of solutions and their applications are introduced. Modeling with higher-order, and systems of linear first-order differential equations are also covered. Numerical methods are covered throughout the course. At the end, students learn series solutions of ordinary differential equations and evaluate how it is applied in real life.

The course covers the standard data representation and algorithms to solve computing problems efficiently (with respect to space requirements and time complexity of algorithm). This covers the following: Lists, Stacks, Queues, Trees, Graphs. Thorough discussion of searching, sorting and hashing. At the end of this course, the students are expected to create a narrative video that tackles real-life example of data structure and algorithms.

This course is the introduction and overview of basic computer organization. Topics include Computer arithmetic: binary, hexadecimal and decimal number conversions, binary number arithmetic and IEEE binary floating-point number standard. Basic computer logic: gates, combinational circuits, sequential circuits, adders, ALU, SRAM and DRAM. Basic assembly language programming, basic Instruction Set Architecture (ISA), and the design of single cycle CPU. Students are expected to develop a computer program simulating how to solve a particular problem using concepts in basic assembly language programming.

The course is an introduction to concepts and techniques in Robotics using Arduino Technology. Topics covered include electricity and electronics, analog and digital signals, basic test instruments, Arduino for robotics, components assembly, sensors, and programming. Upon course completion, one major learning output will be developed, a type of robot explorer capable of following given tracks.

Cameras and digital sensors becoming integral parts of the emerging technologies, it is vital for computer and data scientists to master various techniques for analyzing and processing data from these sensors. This course aims to equip students with a robust understanding of digital image processing methods and algorithms used to enhance images and video streams. Throughout the semester, students will explore topics such as image digitization, image representation, convolution filters, 2D and 3D signal processing, Fourier transforms, image sampling and resampling, grayscale operations, segmentation, morphology, thinning, edge detection, image restoration, biomedical imaging, and image compression. Concepts will be presented through lectures and practical examples, and projects will offer hands-on experience in applying these concepts to real-world challenges.