Engineering Data Analytics Certificate

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The Engineering Data Analytics undergraduate certificate that will prepare future Swanson School of Engineering students to take on the challenge of advancing and innovating engineering applications using the systematic acquisition, management, and analysis of data. In recent years, advances in computing, communication, and data have given rise to data analytics and digital transformation. These new fields are transforming manufacturing, communication, monitoring, and the production of new engineering applications in what has been referred to as Industry 4.0. Data analytics and digital transformation are disrupting industrial practices by the ease of acquiring data in a wide range of applications, combined with advances in distributed and high-performance computing. As a result, data analytics has been identified as the next frontier for innovation, competition, and productivity.

This certificate will require the completion of 15 credits distributed in five different domains across three different levels. An explanation of the levels and domains is provided.

1. Foundations. The foundation courses provide students with programming capabilities and think about data in a statistics framework. Programming courses will focus on Python or R. Students complete two courses that cover two separate domains to complete the foundations. The first domain is Data Science Programming, and the second domain is Inferential Statistics.

2. Expertise. Students will develop skills in the description and analysis of data sources of variability and critical relationships, the development of algorithms and data handling skills to extract and interpret information from complex data sets, and the visualization and communication of results. To complete the expertise level, students complete two courses covering two domains. The first domain is Exploratory Data Analysis, and the second domain is Modeling and Prediction.

3. Specialization. Students will work on a real-world problem with the complications of messy data, ambiguity, and lack of clear structure. This experience should include working with others with diverse skill sets and communicating with non-specialists. This requirement may be satisfied with a capstone project or a faculty-guided research project. Before taking the capstone or research course, students will meet with their respective undergraduate program director and certificate director (housed in Industrial Engineering) to verify the nature of the project can serve as the specialization course for the certificate. Students can work with instructors to tailor their discipline-specific capstone project to include a significant data analytics component (programming in R or python and some data analysis) to satisfy this requirement. Further, if the project is a capstone, the student will confirm with their program coordinator that the capstone may double for both the certificate and their senior capstone.  

From these three levels, students will:

1. Gain an understanding and ability to apply engineering data analytics across these three levels,
2. Obtain practical, real-world experience from the specialization level, and
3. Upon graduation, gain employment or pursue a graduate degree in data analytics.

We anticipate new and applicable courses may be added and approved in the future, such as a proposed class in industrial engineering (IE 1xxx Introduction to Optimization for Machine Learning). Further, as approved by the certificate director, students may take certain Carnegie Mellon University classes through cross-registration to satisfy the certificate requirements. Lastly, students will be required to take at least nine credits beyond the courses required for their major.