<![CDATA[Programming proficiency is a crucial core competence within the computer science education curriculum; however, students frequently encounter significant obstacles in mastering both logic and technical coding execution. This research aims to empirically analyze the influence of Algorithm and Data Structure course grades on students' programming ability using a quantitative approach through the Multiple Linear Regression method. The urgency of this study lies in the necessity for an accurate predictive model to identify students' academic performance at an early stage. This study utilizes simulated data from 100 students who have completed the relevant foundational courses. Statistical analysis results indicate that, simultaneously, Algorithm and Data Structure grades have a significant and positive impact on programming proficiency (p<0.05). Partially, the Data Structure variable contributes slightly more than the Algorithm variable, suggesting that efficient data organization is a critical determinant of practical programming quality. The research findings reveal a coefficient of determination (R2) of 0.8138, indicating that 81.38% of the variation in programming ability can be accurately explained by these two independent variables, while the remainder is influenced by other external factors. The conclusion of this study confirms that mastery of logical foundations and data organization serves as a primary predictor of programming success. Practically, educational institutions can implement this model as an early warning system to provide appropriate academic interventions for at-risk students, ultimately enhancing the quality of graduates in the information technology sector.]]>
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