Data Science Program | St. �山ǿ��

St. �山ǿ��

Data Science

With an interdisciplinary approach, St. Bonaventure's Data Science program will help you develop the mathematical, programming and machine learning skills necessary to produce and clearly explain data-driven conclusions.

Demand for Data Scientists is on the rise. According to the , employment of Data Scientists is projected to grow 35 percent from 2022 to 2032, much faster than the average for all occupations.

At St. Bonaventure, our Data Science majors develop the necessary computer, mathematics and domain knowledge skills needed to fully leverage today's complex collection of data and hardware/software technologies.

The Data Science program offers the ambitious student not only the prospect of a lucrative salary in a growing field, but also work that often greatly benefits people — whether it’s automated medical diagnoses, better retraining methods for the unemployed, more efficient energy distribution, or the granulated study of the efficacy of education programs.

Pictured_Student research_machine learning algorithm

Program information

Bachelor of Science in Data Science

The Data Science program provides students with thorough training in developing the mathematical, programming and machine learning skills necessary to produce and clearly explain data-driven conclusions. Importantly, in such a quickly evolving field, students will also be prepared for ongoing research and learning after they graduate.

The goals and learning outcomes of the Data Science program at St. �山ǿ��:

Goal 1. Students will be able to compare methods for responsibly extracting knowledge from various forms of data.
a) Students will be able to discriminate between different types of data.
b) Students will be able to name and classify different statistical and computational approaches for analyzing large datasets.
c) Students will be able to explain the pros and cons of different approaches to knowledge extraction – particularly in terms of fairness, accountability, transparency, and ethics.

Goal 2. Students will be able to produce and clearly explain data-driven conclusions developed using their skills in programming, machine learning, statistics, and visualization.
a) Students will be able to explain all the steps involved in completing a data science project.
b) Students will be able to collect and clean data to prepare it for knowledge extraction.
c) Students will be able to select and apply industry-standard methods and tools to produce data-driven conclusions using their skills in programming, machine learning, and statistics.
d) Students will be able to explain how each tool works in theory to accurately interpret output.
e) Students will demonstrate proficiency in using data visualization tools and techniques to effectively communicate insights derived from data analysis.
f) Students will apply their skills to real-world projects that aim to solve complex problems across various domains, while emphasizing fairness, accountability, transparency, and ethics.

Goal 3. Students will be able to survey and assess changes in this rapidly evolving field that quickly antiquates any fixed set of skills.
a) Students will locate and reflect on recent data science projects and research.
b) Students will formulate personal plans for ongoing research and learning.

�山ǿ�� students show initiative in solving records-storage problem

May 01, 2024 | Determining a way to manage inactive records wasn’t part of his job description, but Jacob Brockel, a junior Accounting major from Allegany, New York, tackled the problem anyway.

Foerst to give keynote at synod of U.S. Presbyterian Church

Apr 11, 2024 | Dr. Anne Foerst, professor of computer science and program chair, will deliver the keynote address at the annual synod of the U.S. Presbyterian Church in Chicago on April 20.

�山ǿ�� to receive $450,000 to develop Cyber Operations Center

Mar 27, 2024 | St. �山ǿ�� will receive $450,000 in federal funding to develop a Cyber Operations Center on campus. The university would bring actual cyber-attack material into a configured, closed-network environment and use that material for hands-on learning regarding attack prevention and detection/response techniques.

Dr. Chris Bopp

Chair of Data Science Program

Assistant Professor of Computer Science

(716) 375-2083

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Walsh Science Center 113