Data Science and Engineering (BS)
The Data Science and Engineering Data Science major provides students with the knowledge and skills throughout the data science lifecycle. It covers the topics of data structure, discrete systems, algorithms and complexity, database systems, data ethics, big data analytics, and machine learning. Students will learn advanced data science courses with applications in various scientific and engineering domains. They will be able to apply what they learn to many data science application areas including manufacturing, pharmaceutical science, biomedical engineering, aerospace, and chemical and material engineering, and many other emerging areas.
Location
- Storrs Campus
Modality
- In Person
Requirements
| Course | Title | Credits |
|---|---|---|
| Required CSE Courses | ||
| Data Science and Engineering majors are required to complete the following Computer Science and Engineering (CSE) courses: | ||
| CSE 1010 | Introduction to Computing for Engineers | 3 |
| CSE 2050 | Data Structures and Object-Oriented Design | 3 |
| CSE 2500 | Introduction to Discrete Systems | 3 |
| CSE 2600 | Introduction to Data Science and Engineering | 3 |
| CSE 3000 | Contemporary Issues in Computer Science and Engineering | 1 |
| CSE 3140 | Cybersecurity Lab | 2 |
| CSE 3500 | Algorithms and Complexity | 3 |
| CSE 4502 | Big Data Analytics | 3 |
| CSE 4701 | Principles of Databases | 3 |
| CSE 4820 | Introduction to Machine Learning | 3 |
| CSE 4939W | Computer Science and Engineering Design Project I | 3 |
| CSE 4940 | Computer Science and Engineering Design Project II | 3 |
| Additional Required Courses | ||
| All Data Science and Engineering majors must also complete the following: | ||
| MATH 2110Q | Multivariable Calculus | 4 |
| MATH 2210Q | Applied Linear Algebra | 3 |
| Select one of the following: | 3 | |
| Probability | ||
| Statistical Methods | ||
| Probability Models for Engineers | ||
| Introduction to Mathematical Statistics I | ||
| Select one two-semester laboratory course sequence from either chemistry or physics | 8 | |
| General Chemistry I and General Chemistry II | ||
| Enhanced General Chemistry I and Enhanced General Chemistry II | ||
| Honors General Chemistry I and Honors General Chemistry II | ||
| General Physics with Calculus I and General Physics with Calculus II | ||
| Physics for Engineers I and Physics for Engineers II | ||
| Fundamentals of Physics I and Fundamentals of Physics II | ||
| Select one additional science course from the following list (but not in the same department as the two-semester sequence) | 4 | |
| Principles of Biology I | ||
| Principles of Biology II | ||
| Introduction to Botany | ||
| General Chemistry I | ||
| General Chemistry II | ||
| General Physics with Calculus I | ||
| General Physics with Calculus II | ||
| Physics for Engineers II | ||
| Fundamentals of Physics I | ||
| Fundamentals of Physics II | ||
| Earth's Dynamic Environment | ||
| Earth's Dynamic Environment (Lecture) and Earth's Dynamic Environment (Laboratory) | ||
| Select a minimum of four courses totaling a minimum of 12 credits from the following list. 1 | 12 | |
| Introduction to Software Engineering | ||
| Introduction to Cloud Computing | ||
| Introduction to Cryptography and Cybersecurity | ||
or CSE 5850 | Introduction to Cyber-Security | |
| Bioinformatics | ||
or CSE 5800 | Bioinformatics | |
| Numerical Methods in Scientific Computation | ||
or ECE 3431 | Numerical Methods in Scientific Computation | |
| Special Topics in Computer Science and Engineering | ||
| Independent Study in Computer Science and Engineering | ||
| Artificial Intelligence | ||
| Computer Vision and Machine Learning for Image Analysis | ||
| Special Topics in Computer Science and Engineering | ||
| Data Visualization and Communication | ||
| Data Mining | ||
| Reinforcement Learning | ||
| Machine Learning Methods for Biomedical Signal Analysis | ||
| Introduction to Digital Signal Processing | ||
| Image Processing Systems Laboratory | ||
| Elementary Stochastic Processes | ||
or MATH 3170 | Elementary Stochastic Processes | |
| Total Credits | 67 | |
- 1
With prior approval by one of the program coordinators and the School Director, CSE 4095 Special Topics in Computer Science and Engineering and CSE 5095 Special Topics in Computer Science and Engineering and up to 3 credits of CSE 4099 Independent Study in Computer Science and Engineering, CSE 4900 Independent Design Laboratory, or CSE 4997 Senior Thesis in Computer Science and Engineering may be included towards the 12-credit requirement.
Students select additional elective courses to reach a minimum of 120 credits.
College of Engineering Degree Requirements
Students must meet a set of requirements established by the college in addition to the University's Common Curriculum requirements. For more information, see the College of Engineering section of this catalog.
University Common Curriculum Requirements
Every student must meet a set of core requirements to earn a baccalaureate degree, in addition to those required by the student's major course of study and other requirements set by the student's school or college. For more information about these requirements, please see Common Curriculum Requirements.
Learning Objectives
- Analyze a complex computing problem and apply principles of computing and other relevant disciplines to identify solutions.
- Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.