B.S. in Data Science

Program Overview

The curriculum of the Bachelor of Science with a major in Data Science combines courses from the disciplines of Statistics, Mathematics, and Computer Science to prepare students for careers in Big Data Science and Analytics, a rapidly growing field with huge unmet demand. The unique interdisciplinary educational experience allows students to acquire the broad base of knowledge and skills that employers are seeking.

B.S. in Data Science (UVM Catalogue)

Curriculum

A minimum of 120 credits is required. Students are required to complete a minimum of 3 cr. Professional Development Electives that are listed on the College Requirements page.

• Curriculum for the B.S. in Data Science

Outcomes

Learning Outcomes

On graduation, all BS DS students should be able to:

• Design well-modularized and logically-organized programs 
• Implement correct and computationally efficient programs 
• Use and apply fundamental principles of data structures & algorithms
• Use and apply fundamental principles of data management 
• Develop a research question and an effective plan to investigate it using data.
• Plan effective strategies for collecting, processing, and cleaning data to ensure accuracy, completeness, and consistency.
• Use appropriate software tools to import, manipulate, and transform data from various sources. Data wrangling skills include formatting, reshaping, and merging.
• Visualize data to communicate findings accurately and effectively to both technical and non-technical audiences.
• Select appropriate statistical analyses that address a specific research question.
• Execute statistical analyses using professional software.
• Build and assess data-based models.
• Interpret and effectively communicate the results of a statistical analysis through oral and written reports.
• Explain ethical, legal, and social implications of data science and be able to apply ethical principles in data collection, storage, analysis, and sharing.
• Collaborate effectively with individuals of different backgrounds and strengths.

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Vermont Complex Systems Center

The Vermont Complex Systems Center is guided by a post-disciplinary team of faculty and students working at the University of Vermont's College of Engineering and Mathematical Sciences on real-world, data-rich, and meaningful complex systems problems of all kinds.

Vermont Complex Systems Center

Statistics and Data Science Club

This club is a space for students of all majors to learn more about the workings and applications of statistics and data science. It is a registered student chapter of the American Statistical Association. 

We will take advantage of our ASA membership by providing students with networking and career opportunities within the ASA. Also, using public/provided data sets, we will seek to improve our applicable skills while also getting involved with the Burlington/Statistics community.

Statistics and Data Science Club 

Data Science Program Mission

UVM’s undergraduate data science program is committed to fostering a vibrant learning environment that equips students with the knowledge, skills, and ethical values to excel in the dynamic field of data science. Our mission is to provide an exceptional undergraduate education in data science, preparing our students to become data-driven problem solvers, innovators, and responsible global citizens.

Our Data Science Degree Program is dedicated to achieving the following core objectives:

1. Excellence in Education: We aim to deliver a comprehensive and rigorous curriculum that combines foundational theory and practical applications. Our students will develop a deep understanding of mathematics, statistics, computer science, and domain-specific knowledge, providing them with the tools to analyze, interpret, and derive insights from complex data.
2. Interdisciplinary Approach: We recognize that data science is inherently interdisciplinary. Our program fosters collaboration across various fields, encouraging students to work with diverse data types and domains, from healthcare and finance to social sciences and engineering.
3. Innovation and Research: We encourage our students to engage in cutting-edge research and creative problem-solving, enabling them to develop innovative data-driven solutions. Through faculty mentorship and access to state-of-the-art technology, our graduates will be well-prepared to contribute to the ever-evolving field of data science.
4. Ethical and Responsible Data Use: We instill in our students a strong sense of ethical responsibility when handling data. They will understand the importance of privacy, transparency, and fairness in data science practices, ensuring their work benefits society and individuals.
5. Real-world Experience: Our program incorporates hands-on experiences, internships, and capstone projects that connect students with industry partners. This practical exposure allows students to apply their knowledge to real-world challenges and build a network within the data science community.
6. Inclusivity and Diversity: We are dedicated to fostering an inclusive learning environment that embraces diversity of thought and backgrounds. We believe that a diverse student body leads to richer perspectives and solutions in the data science field.
7. Lifelong Learning: We emphasize the importance of continuous learning in the rapidly evolving data science landscape. Our graduates will be equipped with the skills and motivation to adapt to new technologies and methods throughout their careers.
8. Community Engagement: We encourage our students to engage with the broader community through outreach, volunteer work, and educational initiatives, recognizing that data science has the potential to address societal challenges.

Through a commitment to these principles, we prepare our graduates for fulfilling careers in data science, empowering them to make data-driven decisions that positively impact their organizations, communities, and the world at large. Our data science degree program strives to cultivate the next generation of data scientists who are versatile, ethical, and prepared to tackle the data challenges of tomorrow.