Building Practical Knowledge of the Data Science Lifecycle

Northwestern Engineering students pursuing the machine learning and data science (MLDS) minor learn to develop comprehensive data science pipelines, glean insights from data, and think critically about data-driven decision-making.

The new codirectors of the MLDS minor, Moses Chan and Huiling Hu, credit the success of the program, in part, on the multidisciplinary backgrounds and interests of the student cohorts, composed of students from all nine departments at the McCormick School of Engineering.

“I am constantly impressed by the lateral critical thinking that is made possible by the composition of students in our minor,” said Chan, assistant professor of instruction in industrial engineering and management sciences at Northwestern Engineering. “For example, one group of students is examining the impact of air pollutants on the climate, whereas another group is analyzing performance data from Formula 1 racing and NCAA/NBA basketball. The unique perspective each student brings to the classes has enriched the experience of all the students in the minor.”

MLDS minor students such as Joey Patronik (’23), now an industrial engineering analyst at United Airlines, build skills in computational data analysis to estimate, predict, design, and control engineering systems, and apply that knowledge in internships and industry.

“In my work in the airline industry, data isn’t always clean, it comes from various sources, and I might not have my platform of choice to analyze it,” Patronik said. “The Machine Learning and Data Science program equipped me with the necessary skills to adapt to new situations and effectively utilize the tools and platforms used at my job. These skills are crucial because I collaborate with a larger team to extract insights from data to support various units within the company, whether they be at the airport or elsewhere.”

Hannah Wilks, a third-year student in mechanical engineering at Northwestern Engineering, gained hands-on data science skills that helped her tackle a summer internship project at Lightshift Energy, a utility-scale energy storage development company.

“I built a database, developed an algorithm, and integrated an API for battery energy system project development. Without the MLDS minor’s foundational courses, completing my project would have been much more difficult,” Wilks said. “Moreover, as the tech world continues to change, my data science and machine learning skills — from data analysis and regressions to a deep understanding of machine learning algorithms — are all incredibly useful as I enter the workforce.”

Hu, an assistant professor of instruction in computer science, underscored that data science and machine learning techniques are useful in almost every discipline.

“The techniques we cover in this minor provide students with comprehensive and in-depth toolkits for many data science and machine learning tasks,” Hu said. “These skills are useful in their future careers and can improve their productivity even during school years.

Expanded curriculum tracks

Launched by the Department of Industrial Engineering and Management Sciences and Department of Computer Science in fall 2021 as the data science and engineering minor, MLDS expanded last fall to offer students greater breadth and flexibility through three specialization tracks: machine learning, data science, and a hybrid option.

• Data Science Track: Students learn how to design and develop data pipelines to extract, clean, and analyze data. Students also explore strategies to leverage data-driven insights, evaluate data quality, and implement scalable data storage solutions.
• Machine Learning Track: Designed for non-CS majors, students apply algorithms such as linear/nonlinear regression, neural networks, and decision trees to train models on large datasets. Students also learn foundational principles of artificial intelligence, including knowledge- and search-based methods for problem solving and inference.
• Hybrid Track: This track combines foundational skills in both machine learning and data science with a four-course sequence including COMP_SCI 214 Data Structures and Algorithms, COMP_SCI 349 Machine Learning, DATA_ENG 200 Foundations of Data Science, and DATA_ENG 300 Data Engineering Studio.

The core curriculum of the MLDS minor emphasizes fundamental computational and statistical methods. Through two MLDS electives, students can explore topics including computational genomics, deep learning, information visualization, optimization, and statistical pattern recognition.

In addition, students have an opportunity to connect with their minor cohort through social events, such as a spring finish-line celebration of minor graduates, and join career-building events, including data science industry panels and data practitioners guest speaker seminars.

Over four cohorts, approximately 350 students have been admitted to the minor. Chan and Hu are excited to continue strengthening the program, acknowledging the support of the MLDS minor steering committee and leadership of founding codirectors Jennie Rogers, associate professor of computer science, and Jill Wilson, assistant chair and professor of instruction of industrial engineering and management sciences in Northwestern Engineering.

To learn more about the MLDS minor and receive information regarding an upcoming info session, please sign up for the program newsletter.