Introduction to Computing in Engineering
As a degree requirement, majors within the Tufts School of Engineering have a computing requirement. Students should check their major/intended major(s) to see how ES 2: Introduction to Computing in Engineering can be used to satisfy the degree requirements. For more details on different majors for particular class years, please visit the Engineering Degrees webpage.
Note: students that come to Tufts with prior programming experience may be prepared to enroll in a second course in computing instead of ES 2; see the Computer Programming Placement Exam page for more information about this option.
Course Information
The Tufts University Course Catalog description of ES 2 is currently as follows: “Computational fluency through common code concepts, data types and data structures, program flow, and algorithmic implementation. Application to engineering projects including mathematical operations, numerical error calculations, and real-world data analysis. Data science concepts exploring data cleansing, descriptive statistics, analysis and visualization of data sets, and modeling and optimization of physical systems through code. Sociotechnical case studies explore the implications of computing and engineering design choices.”
Course Attribute: SOE-Computing
Students may receive credit for either ES 2 or CS 10, but not both.
Recommendation: MATH 32
The content of ES2 is designed to achieve three main goals: (1) Fluency in a computer language, (2) Understand tools for engineering computing, (3) Applying these tools to data analysis, and (4) Implementing sociotechnical analyses. The achieve these goals, the ES 2 course strives to include all of the following key components:
Fluency in a computer language
- – Master basic coding concepts
- – Know common commands and data types
- – Use good code style
- – Plan both small and medium-scale projects
Understand tools for engineering computing
- – Quantify numerical error in solutions
- – Know how to use symbolic math tools
- – Understand matrix/vector calculations
- – Know how to leverage built in help resources
Apply these tools to data analysis
- – Fit curves/models to noisy data
- – Apply descriptive statistics to datasets
- – Work with a variety of data formats
- – Have exposure to modeling physical systems in code
Implementing sociotechnical analyses
- – Evaluate different impacts of computing-based technologies
- – Examine the social, economic, and policy aspects of engineering
- – Recognize and reduce bias in data and algorithms
- – Identify inherent limitations of computing solutions
ES 2 Sections for Spring 2026:
| Mathematica | Matlab | Python |
| TBD | TBD | TBD |
Section information will be released at end of October during pre-registration advising, once finalized in SIS.
Note: each section of ES 2 meets a required 3x times a week for 75-minutes each class session
ES 2 Section 01: TBD (Language: TBD)
ES 2 Section 02: TBD (Language: TBD)
ES 2 Section 03: TBD (Language: TBD)
ES 2 Section 04: TBD (Language: TBD)
ES 2 Section 05: TBD (Language: TBD)
ES 2 Section 06: TBD (Language: TBD)
ES 2 Section 07: TBD (Language: TBD)