The amount of data generated from industrial processes has dramatically increased in recent times. As a result, data analytics skill has become an essential requirement for industrial engineering jobs. To meet this requirement, universities and colleges are beginning to integrate data analytics into industrial engineering curriculum. However, teaching and learning data analytics to industrial engineering students is by no means an easy task, since both programs have diverging focus. Industrial engineering focuses on process and systems optimization while data analytics focus on the application of information technology and mathematical models to visualize and extract useful information from raw data. To support teaching and learning of data analytics to industrial engineering students, this innovative practice full paper reports a pedagogical method that extrapolates product manufacturing processes to teaching and learning data analytics. We selected product manufacturing because it is a core course in the industrial engineering curriculum. The proposed pedagogical method is developed by first analyzing and comparing product manufacturing processes and data analytics techniques. Afterwards, we used the result of this analogy to develop a teaching and learning method for data analytics. For implementation and validation purposes, we adopt a project-based learning approach where students used our methodology to complete real-world data analytics projects. Data from students' grades shows that this approach improved their performance.