This study aims to enhance student learning in the introductory data science course by combining Project-Based and Challenge-Based Learning methodologies. The goal is to improve student performance, engagement, and comprehend of data science subjects through research and development. By integrating challenge-based learning (CBL) and project-based learning (PBL) into a cohesive teaching approach, the benefits of both approaches are employed to improve the learning experience for students. This methodology facilitates in-depth comprehension, critical reasoning, and practical application of data science skills, preparing students for academic success and real-world obstacles. The research sample comprised 56 third-year undergraduates from the Computer Science department, selected using a purposive sampling method. Data analysis involves the utilization of several statistical techniques such as the t-test, analysis of covariance (ANCOVA), mean, standard deviation, and other relevant statistics. The findings indicated that students who utilized a blend of Project-Based and Challenge-Based Learning methods had a notable increase in academic performance post-learning, as compared to their pre-learning performance, with statistical significance. Firstly, students at the 05 level are more likely to express their satisfaction with the course of study. This is primarily because the course incorporates problem-based learning (PBL) and case-based learning (CBL), which are both engaging and practical teaching methods. Furthermore, this implies that improving student achievement in the data science course is an effective use of the integrated teaching methodology.