In the era of big data, many business organizations consider data analytics skills as important criteria in the acquisition of qualified applicants. As numerous managerial decisions in the field of marketing are becoming evidence-based, business schools have integrated case studies about different stages of data analytics such as problem identification, data collection, data processing, data analysis and data visualization in order to improve the knowledge of marketing students. Although case studies can provide a good theoretical foundation about data analytics in the field of marketing, but they may not be sufficient for building analytical skills from a technical perspective. This paper provides a guideline on how Python as a programming language can be used to explore large datasets and improve marketing students' capabilities with a focus on data processing, data analysis and data visualization tasks. In this research, a survey was conducted to measure the teaching effectiveness and overall satisfaction of marketing students (n = 84) in a Canadian university. The evidence suggests that Python libraries designed for marketing-related data analysis and data visualization have positive outcomes in students' learning experience and perception of teaching effectiveness.