Principles of Computational Thinking for Engineers
Engineering practice increasingly relies on computational tools and data analysis approaches. The course introduces computational thinking into engineering analysis. Integrates data science and statistics, linear algebra, artificial intelligence, and mathematical modeling approaches into the context of contemporary problems in the design and analysis of processes, products, and systems. Focuses on paradigms, not syntax. Use of the Julia programming language and its associated toolchain to transition from idea to implementation. In addition, cloud computing resources for course materials, assignments, and projects. Weekly labs provide guided hands-on practice. Course assignments use data sets and examples from industrial practice to develop fluency and understanding of real-life problems.
Topics
- Demonstrate mastery of basic software engineering paradigms, data structures, common programming idioms, and algorithms.
- Analyze scientific, engineering, and financial data sets using tools from data science/statistics and machine learning (ML).
- Identify and test quantitative models of process and product performance using real-time dynamic and static data sets
- Demonstrate mastery of quantitative decision-making and risk management approaches in the context of a process, product, or system design.
Official roster learning outcomes
Prerequisites & course information
background equivalent to: CS 1110 or CS 1112.
Sources
- Course catalogue ↗2026-2027