In response to the growing demand for interdisciplinary computer science (CS) expertise, this paper presents the design and planned implementation of a cross-listed course that integrates fundamental data structures and algorithms with modern parallel computing concepts. The course targets two distinct student populations: senior CS undergraduates seeking advanced electives and conditionally admitted graduate students in the Master of Science in Computer Science (MSCS) program who require a leveling course to address prerequisite gaps. Recognizing that traditional leveling courses such as CSC340 are not always available each semester and often lack emphasis on practical, real-world applications, this course was designed to provide a flexible and robust alternative. It incorporates theoretical foundations and hands-on programming with OpenMP and MPI, covering key topics including sorting algorithms, trees, graphs, topological sort, search techniques, and performance metrics like speedup and efficiency. Instruction is structured around active learning strategies—teamwork and unplugged exercises—that enhance students' conceptual understanding and problem-solving skills. Undergraduate students engage in exams and projects that reinforce algorithmic reasoning and collaborative implementation. Graduate students complete two in-depth projects emphasizing both algorithmic rigor and research-readiness. Differentiated assignments challenge both cohorts appropriately. The course also serves a strategic curricular function: it meets elective needs for undergraduates and substitutes for required graduate prerequisites. A comprehensive evaluation plan includes student feedback, supplemental questionnaires, pre- and post-assessments for graduate students, and follow-up interviews to assess long-term outcomes and the course's effectiveness as a bridge to advanced study. This course represents a practical model for institutions aiming to address both curriculum flexibility and the growing importance of parallel computing skills across undergraduate and graduate education. Future work will explore integrating emerging technologies, refining instructional strategies, and incorporating real-world datasets through industry collaboration to further align with workforce needs.

Keywords: Parallel computing, data structures, algorithms, OpenMP, MPI, computer science education.