Data Science and Engineering
The McCormick minor in Data Science & Engineering provides students with practical knowledge fundamental to the data science lifecycle. Students will gain experience with a variety of data models and techniques used for collecting data, cleaning it, and analyzing it. They will also learn how to glean insights from data through multiple modern computational tools, as well as the ability to think critically about the construction and implications of analysis and models for data-driven decision making.
The minor is anchored by a pair of specialized studio courses. Foundations of Data Science launches the student experience and provides hands-on training with the tools needed to work effectively with large data sets in the cloud or on their own machines. Their studies culminate with Data Engineering Studio, where they will synthesize their in-depth knowledge of statistics, machine learning, and computing with a quarter-long project using real-world data. This project will enable them to apply their skills to a sustainable, reproducible manner. This program also includes two elective courses to either enrich each student's major study or broaden their experience in data-intensive analysis in other disciplines.
The minor is designed to be accessible to students across Northwestern Engineering. It will empower them to develop comprehensive data science pipelines, using computational data analysis for the estimation, prediction, design, and control of engineering systems.
Programs of Study
Data Science and Engineering Courses
DATA_ENG 200-0 Foundations of Data Science (1 Unit) This course will cover the fundamentals of data science and the context within which this field operates. Students will learn how to design their data analysis by learning to think critically about what questions are answerable with data and they will learn about common pitfalls in data analytics such as algorithmic bias and best practices for handling the sensitive data of others. It will also introduce students to computational thinking, a methodology for problem-solving the technological challenges they will encounter as data scientists. This course will also introduce the steps of the data science lifecycle and common tools and techniques for data science. We will cover data exploration, the principles of data cleaning and integration, version control, and building reproducible data science pipelines. This course is reserved for students pursuing the McCormick Data Science and Engineering Minor. We encourage students to take this early in their studies for the minor. It is the first part of a two-part sequence with DATA_ENG 300-0. Prerequisite: COMP_SCI 150-0.
DATA_ENG 300-0 Data Engineering Studio (1 Unit) Data Engineering Studio will teach students how to build a sustainable data science lifecycle. Students will analyze data in multiple contexts (e.g., SQL, building machine learning models), share their findings with peers, and practice iteratively refining their analysis based on feedback from the instructor, course staff, and peers. Students will also hone their practical skills and they will become acquainted with the common pitfalls in applying data analytics to real-world datasets. Moreover, students will learn how to analyze and visualize data from multiple data models, including graph analytics, time series data, and relational data. This course is reserved for students pursuing the McCormick Data Science and Engineering Minor. We encourage students to take this course at the end of their studies in the minor. It is the second part of a two-part sequence with DATA_ENG 200-0. DATA_ENG 300-0 has a “flipped classroom” format. Students are responsible for watching a lecture before each class. Then they will work collaboratively with their teams, the instructor, and course staff to learn how to solve various challenges in the data science pipeline. Prerequisite: DATA_ENG 200-0 and 1 unit from each of the following core areas; Statistics Foundations, Intermediate Programming/Algorithmic Skills, and Applied Machine Learning.