Effective evaluation of teaching functions is paramount for identifying strengths and areas of improvement within the teaching-learning process. Leveraging data science techniques to analyze the results of teaching function surveys unveils patterns, trends, and relationships among variables not discernible through traditional methods. The University of Guadalajara (UDG) has previously implemented a systematic survey comprising nine items for students to evaluate teacher’s teaching performance. However, this approach has limited value in enabling appropriate analysis and feedback to enhance teaching practices. This chapter uses a 26-item data analysis survey to evaluate teaching practices on one campus, the University Center for Economic and Administrative Sciences (CUCEA). The proposed methodology includes survey design and deployment, data collection, cleaning, transformation, and visualization. Initially, the analysis was developed using Excel and Power BI. However, this method proved insufficient. Subsequently, programming functions in Python were created, offering flexibility and capability for robust data analysis, visualization, and distribution. It has been found that the development of a Python program automates the analysis of university teacher evaluation surveys, allowing direct access to results and empowering informed decision-making among educators and the institution. Implementing this model could be a pivotal tool for the UDG to identify teaching challenges and tailor professional development programs to enhance teaching practices effectively.