Program Overview

The minor in Machine Learning and Data Science (MLDS) is designed for Northwestern Engineering students who wish to develop expertise in machine learning, data science, or a blend of both. Students complete the program empowered to apply practical knowledge fundamental to the data science lifecycle, glean insights from data, and think critically about data-driven decision making. Throughout the curriculum, students will gain hands-on experience with visual models and techniques used for collecting, cleaning, and analyzing data. 

Students will take four core requirements during the program. A programming course provides students an opportunity to familiarize themselves with coding programs, like Python. Meanwhile, a course in statistics teaches students key metrics by which to analyze data. Two MLDS electives allow for exploration of students' unique interests, ranging from courses on wearable devices, optimization, to RNA sequencing.  

Finally, the minor features three specialization tracks: Machine Learning*, Data Science, or a Hybrid option. Specializations, chosen during the application process, contain four courses relevant to your chosen topic area. The difference between the two topic areas is as follows:

Machine Learning* Data Science

Machine learning focuses on the creation, training, and the validation of models and algorithms that can make predictions or decisions.

In this specialization, students will learn about algorithms like linear/nonlinear regression, neural networks, and decision trees to train models on large datasets. Students will also learn foundational principles of artificial intelligence, including knowledge- and search-based methods for problem solving and inference.

Machine learning is a key component of a variety of industries, ranging from stock market forecasts to virtual assistant speech recognition.

Data science engineering focuses on building and maintaining the structures required to collect, store, and process large quantities of data. Its scope is broader than machine learning as it extends over the entire data lifecycle, from retrieval to analysis.

In this specialization, students will learn how to design and develop data pipelines required to extract and transform data, ensure data quality, enable data accessibility for analysts, implement scalable data storage solutions, and basic machine learning concepts.

Data science engineering is key to ensuring that high-quality data is accessible for analysis. The data architectures learned in this specialization drive business intelligence and strategy.



Explore the full curriculum

*Note: Students majoring in Computer Science are not eligible for the Machine Learning specialization.