Teaching computer science is challenging because it involves explaining concepts, managing a wide range of student abilities, and most importantly keeping up with rapidly evolving technology. Moreover, teachers have limited resources, pressure to meet academic standards, as well as high expectations from parents and the community. Therefore, teachers need to balance theory with practical skills, exercise diverse learning styles to better explain abstract concepts while promoting problem-solving and critical thinking. This paper presents various strategies to deliver computer science theories and algorithm courses, highlighting their implications on student success. To better understand the benefits of the adopted strategies, graduate level algorithms and data structures and intelligent systems courses are considered for observation. The findings indicate a clear preference for traditional face-to-face instruction and hybrid classes, suggesting that online classes may not align with students’ learning preferences and could negatively impact their academic performance. Additionally, the combination of various teaching and learning styles is highly effective in enhancing student learning experience for theoretical computer science courses. This paper offers suggestions for addressing these challenges by incorporating techniques such as in-class activities, whiteboard demonstrations, timely feedback, increased student-faculty interaction, mentoring, and, most importantly, showing empathy toward students. Both courses received positive feedback, with over 90% of students giving a satisfaction score of 8 or higher out of 10.