Course Description: Computing, robotics, and other electronic applications are made possible by Digital Logic. Electrical engineering and computer engineering both depend on digital logic design. Complex electronic components are created by Digital Logic designers using both electrical and computational properties. Students who complete this course will be able to design and evaluate both sequential and combinational logic circuits. Additionally, they will comprehend the fundamental software programs used in the development of digital circuits and systems.

Course Description: This course will introduce students to topics such combinational and sequential circuit analysis and design, digital circuit design optimization techniques using arbitrary logic gates, multiplexers, decoders, registers, counters, and programmable logic arrays according to the CSE-207 course.

Course Description: Data structures are the techniques of designing the basic algorithms for real-life projects. The practice and assimilation of data structure techniques is essential for programming. Besides the soul of computing is algorithms. The theoretical foundation of computer science is provided by algorithm design and analysis, which are essential to the day-to-day activities of a good programmer. The basic analysis and design techniques for effective algorithms are covered in this course, with an emphasis on techniques that are practical. The course will help students to develop the capability of selecting a particular data structure too.

Course Description: By the end of this course, students will be able to create computer programs using fundamental building blocks like control statements, arrays, functions, pointers, and strings, as well as data structures like stacks, queues, and linked lists, for use in computing and practical applications. Additionally, they will also be introduced with the practical knowledge to implement searching and sorting algorithms.

Course Description: Understanding probability enables you to make well-informed choices regarding the likelihood of events based on a pattern of facts gathered. Statistical inferences are frequently employed in the context of data science to assess or forecast trends from data, and these inferences make use of probability distributions of data. By taking this course, students will gain knowledge of how to use appropriate statistical techniques to gather, organize, display, and analyze pertinent data. They will also learn how to distinguish between different types of data (qualitative, quantitative, discrete, and continuous), different types of sampling (random, stratified, systematic, and cluster), and statistical misuses.