• LPPP 2700

    Outdoor Leadership: Building a Team
     Rating

     Difficulty

     GPA

    3.72

    Last Taught

    Fall 2025

    Leading teams in an outdoor space require a strong team foundation, good risk-management skills, learn to live outdoors, and develop a wide range of outdoor technical skills. An emphasis will be placed on reflection of field time and how content learned in class can apply to a variety of contexts. Learning to lead in outdoor spaces gives leaders tangible leadership practice and a flexible mindset to solve front-country problems.

  • CGBM 2710

    Making Financial Decisions
     Rating

    4.33

     Difficulty

    3.00

     GPA

    3.46

    Last Taught

    Spring 2026

    In this course, students will learn the fundamental building blocks of valuing streams of cash flows whether from a financial asset or investment project. Topics to be covered may include the time value of money, discounting, compounding, investment rules including estimating the net present value of a project, and the basics of capital budgeting.

  • EBUS 2730

    Deals & Negotiations for Engineers
     Rating

    2.00

     Difficulty

    2.00

     GPA

    Last Taught

    Spring 2026

    The course will not only teach the components of doing a transaction but also the skills necessary to negotiate effectively and work with legal partners. Agreement types important to the technology sector will be explored. Students will learn from readings, case studies, projects, and in-class discussions.

  • LPPL 2750

    Teambuilding and Facilitation
     Rating

     Difficulty

     GPA

    3.74

    Last Taught

    Spring 2026

    Effective facilitators are architects of engagement. Take part in practical facilitation, bridging theory, & real-world scenarios. Develop facilitation skills, clear communication, & strategies for effective group development. Emphasis placed on facilitation practice, allowing students to gain experience & insight from collective feedback.

  • UNST 2810

    Introduction to Academic Research
     Rating

    3.44

     Difficulty

    1.00

     GPA

    Last Taught

    Fall 2025

    This course is intended for participants in the Undergraduate Student Opportunities in Academic Research (USOAR) program.

  • UNST 2811

    Introduction to Academic Research Part II
     Rating

     Difficulty

     GPA

    Last Taught

    Spring 2026

    This course is intended for participants in the Undergraduate Student Opportunities in Academic Research (USOAR) program.

  • EBUS 2830

    Innovation and New Ventures
     Rating

    5.00

     Difficulty

    2.00

     GPA

    3.94

    Last Taught

    Spring 2026

    An introduction to concepts innovators use to solve problems and create value by addressing unmet needs. Learn how to identify and evaluate opportunities and use proven entrepreneurial frameworks to create new products and businesses for companies of all sizes. Through class activities, projects, and presentations you will learn how storytelling, teamwork, and leadership skills are essential for starting, funding, and building your business. Prerequisite: EBUS 1800

  • EBUS 2850

    Government and Entrepreneurship
     Rating

     Difficulty

     GPA

    Last Taught

    Spring 2026

    The course explores government contracting, how the government procures products and services, and opportunities created through government regulation. Pre-requisite: STS 1500 or ENGR 1020 or ENGR 2595-Engineering Foundations II.

  • DS 3001

    Foundations of Machine Learning
     Rating

    4.00

     Difficulty

    3.25

     GPA

    3.91

    Last Taught

    Spring 2026

    This course exposes students to foundational knowledge in each of the four high level domain areas of data science (Value, Design, Analytics, Systems). This includes an emphasis on ethical issues surrounding the field of data science and how these issues originate and extend into society more broadly.

  • DS 3005

    Mathematics for Data Science
     Rating

     Difficulty

     GPA

    3.48

    Last Taught

    Spring 2024

    Engage with and train in the use of key concepts in machine learning and math: OLS estimator for regression; logistic regression & maximum likelihood estimator; multiple linear regression; principal components analysis & multiple correspondence analysis; neural networks; logarithms; probability distributions; integrals; multivariate optimization; matrix notation, eigen-math, and matrix decomposition; infinite power series & Taylor series.