Development of a Data Science Curriculum for an Engineering Technology Program

Abstract

Data science has gained the attention of various industries, educators, parents, and students thinking about their future careers. Statistics departments have traditionally offered data science courses for a long time. The main objective of these courses is to examine the fundamental concepts and theories. However, teaching data science courses has also expanded to other disciplines due to the vast amount of data being collected by numerous modern applications. Also, someone needs to learn how to collect and process data, especially from industrial devices, because of the recent development of Internet of Things (IoT) technologies. Hence, integrating data science into the curricula of different engineering branches becomes a matter of relating the statistics background to the specific discipline. There are several reasons for this transition. Firstly, as the increased computational power and massive availability of the data make the use of statistical theories possible in more engineering applications, there is a growing need for engineering students to build knowledge in data science concepts. Secondly, the wide availability of libraries and models allows for the implementation of diverse solutions to engineering problems. This paper will discuss introducing a new data science curriculum in an Engineering Technology (ET) program with a focus on Electrical Engineering Technology (EET) program.The data science courses, i.e., "Introduction to Data Science" and "Advanced Topics in Data Science," will be offered as 400-level courses in the Electrical Engineering Technology (EET) program. These courses introduce the students to the fundamental principles and techniques in data science and machine learning for data collection, processing, analysis, visualization, and data-driven decisions. The introductory course will provide an overview of data science and machine learning and a comprehensive knowledge of their tools, along with hands-on activities. This course will also cover several course activities focused on the data science topics, such as introduction to data science and statistics with Python, data structures, data visualization, regression, classification, and clustering methods, dimensionality reduction, network analysis, computer vision with OpenCV, natural language processing.