Data Science (BS)
Program Director: Sana Spector, spektors@canisius.edu
Introduction
Data is transforming every industry — and the professionals who can collect, manage, analyze, and interpret it are in extraordinary demand. The Bachelor of Science in Data Science at Canisius University prepares you to be one of them.
Our curriculum combines rigorous foundations in programming (Python, R), mathematics (calculus through linear algebra), and statistics with applied coursework in machine learning, econometrics, reproducible data workflows, and AI ethics. You will graduate with the technical skills employers are looking for and the ethical judgment that sets Canisius graduates apart — ready for careers in data engineering, analytics, machine learning, consulting, sports analytics, financial modeling, health informatics, and dozens of other high-growth fields.
What makes our program distinctive:
- A complete, integrated curriculum — from your first line of code to advanced machine learning, with mathematics and statistics woven in at every stage.
- A dedicated course in Data, AI and Ethics (DAT 419) — open to all majors across the university — that ensures every Data Science graduate can think critically about the systems they build.
- A direct 4+1 accelerated pathway to the MS in Data Analytics — start earning graduate credit as an undergraduate.
- Hands-on, project-based learning from day one, with real-world datasets, Git/GitHub workflows, and portfolio-ready deliverables.
- Small class sizes and dedicated faculty advising in the School of Data, Computing & Mathematics.
Careers and Outcomes
Data Science graduates pursue careers in a wide range of industries. Recent Canisius graduates and interns have worked in roles such as:
- Data Analyst and Data Engineer at regional and national firms
- Machine Learning Engineer and AI Developer
- Sports Analytics Analyst for professional and collegiate organizations
- Business Intelligence Analyst in finance, marketing, and healthcare
- Research Analyst in government, nonprofits, and higher education
- Graduate study in data science, statistics, computer science, and related fields
The Bureau of Labor Statistics projects 36% growth in data scientist roles through 2033 — far faster than the average for all occupations. Median annual salary for data scientists exceeds $108,000 nationally.
Qualifications
Students must maintain a 2.0 GPA in the major and a 2.0 overall average to graduate with a degree in Data Science.
Advisement
All students should have an advisor in the major and should contact the department directly to have an advisor assigned if they do not already have one. Meetings with academic advisors are required prior to students receiving their PIN for course registration each semester. All majors should work closely with their advisor in discussing career expectations, choosing their major electives, developing their entire academic program and planning their co-curricular or supplemental academic experiences.
Curriculum
An Ignatian Foundation
All undergraduate students must complete either the Canisius Core Curriculum or the All-College Honors Curriculum. Many schools refer to their college-wide undergraduate requirements as "general education" requirements. We believe that the core curriculum and the honors curriculum are more than a series of required classes; they provide the basis for a Jesuit education both with content and with required knowledge and skills attributes that are central to our mission.
Free Electives
Students may graduate with a bachelor's degree with more but not less than 120 credit hours. Free electives are courses in addition to the Canisius Core Curriculum or All-College Honors Curriculum and major requirements sufficient to reach the minimum number of credits required for graduation. The number of credits required to complete a bachelor's degree may vary depending on the student's major(s) and minor(s).
Major Requirements
| Code | Title | Credits |
|---|---|---|
| Computer Science Courses | ||
| CSC 111 & 111L | Introduction to Programming and Introduction to Programming Laboratory | 4 |
| CSC 112 & 112L | Data Structures and Data Structures Laboratory | 4 |
| CSC 213 | Large Scale Programming | 3 |
| CSC 213L | Large Scale Programming Laboratory | 1 |
| CSC 310 & CSC 310L | Information Organization and Processing and | 4 |
| Data Science Courses | ||
| DAT 111- Intro to Reporting and Analysis | 3 | |
| DAT 211-Intro to Statistics with R | 3 | |
| DAT 411-Econometrics (cross-list current BUS or DAT course, not a new course) | 3 | |
| DAT 412-Machine Learning | 3 | |
| Mathematics Courses | ||
| MAT 111 | Calculus I | 4 |
| MAT 112 | Calculus II | 4 |
| MAT 211 | Calculus III | 4 |
| MAT 219 | Linear Algebra | 4 |
| MAT 351 | Probability & Statistics I | 3 |
| MAT 341 | Numerical Analysis | 3 |
| Total Credits | 50 | |
A minor or double major is required for this program in areas such as Computer Science, Mathematics, Psychology, Business, or Marketing. Other areas of studies may be selected based on approval from program director.
Roadmap
| Freshman | |
|---|---|
| Fall | Spring |
| CSC 111 & 111L | CSC 112 & 112L |
| MAT 111 | MAT 112 |
| Elective | |
| Sophomore | |
| Fall | Spring |
| CSC 310 | DAT 111 |
| MAT 211 | Minor Course 1 |
| DAT 211 | Minor Course 2 |
| CSC 213 & 213L | |
| Junior | |
| Fall | Spring |
| MAT 219 | MAT 341 |
| Elective | MAT 351 |
| Elective | DAT 413 |
| Senior | |
| Fall | Spring |
| DAT 411 | DAT 412 |
| Elective | DAT 419 |
| Minor Course 3 | |
| Minor Course 4 | |
Learning Goals and Objectives
Data Science: Program Learning Goals
The BS in Data Science is built around four program learning goals. Every required course contributes to one or more of these goals, and students demonstrate mastery through coursework, projects, and presentations across the curriculum.
Goal 1 — Computational and Analytical Foundations
Students will design, implement, and evaluate computational solutions to data-intensive problems using appropriate programming languages, data structures, algorithms, and mathematical methods.
- 1A: Write programs in Python and R to collect, clean, transform, and analyze structured and unstructured data.
- 1B: Apply calculus, linear algebra, probability, and numerical methods to formulate and solve analytical problems in data science.
- 1C: Design and query relational databases using SQL; manage data pipelines from acquisition through analysis.
Goal 2 — Statistical Reasoning and Machine Learning
Students will apply statistical and machine learning methods to extract knowledge from data, evaluate model performance, and make evidence-based decisions.
- 2A: Conduct exploratory data analysis and apply descriptive and inferential statistical methods to real-world datasets.
- 2B: Build, train, evaluate, and interpret supervised and unsupervised machine learning models.
- 2C: Select appropriate methods for a given problem, justify modeling choices, and communicate the uncertainty and limitations of results.
Goal 3 — Communication and Data Storytelling
Students will communicate data-driven findings effectively to technical and non-technical audiences through writing, visualization, and oral presentation.
- 3A: Create clear, accurate, and compelling data visualizations using appropriate tools (ggplot2, Tableau, Power BI, interactive dashboards).
- 3B: Write well-organized reports and documentation using reproducible workflows (R Markdown, Quarto, GitHub).
- 3C: Deliver oral presentations that convey analytical results and their implications to diverse audiences.
- 3D: Collaborate effectively on data projects using version control (Git/GitHub) and team-based workflows.
Goal 4 — Ethical Data Stewardship
Students will critically evaluate the social, legal, and ethical dimensions of data collection, algorithmic decision-making, and AI deployment, and will apply ethical reasoning to their professional practice.
- 4A: Identify and analyze sources of bias in data, algorithms, and deployment contexts.
- 4B: Apply ethical frameworks (consequentialist, deontological, virtue ethics, Jesuit cura personalis) to real-world AI and data case studies.
- 4C: Describe the legal and regulatory landscape of data and AI (GDPR, CCPA, FERPA, HIPAA, EU AI Act) and apply these principles to professional decision-making.
- 4D: Conduct an ethics audit of a real-world AI system, identifying stakeholders, harms, and responsible mitigation strategies.
Courses
Computer Science
CSC 111 Introduction to Programming 3 Credits
Algorithms, programming, computers, and applications to problem solving in Python.
Corequisite: CSC 111L.
Fulfills College Core: Field 7 (Mathematical Sciences)
Offered: every fall & spring.
CSC 111L Introduction to Programming Laboratory 1 Credit
Required lab for CSC 111.
Corequisite: CSC 111.
Offered: every fall & spring.
CSC 112 Data Structures 3 Credits
Introduction to object-oriented programming, recursion, and data structures, including lists, stacks, queues, trees and maps. Rudimentary discussion of analysis of algorithms. Python language is used.
Prerequisite: minimum grade of C in CSC 111 & CSC 111L. Corequisite: CSC 112L.
Offered: every spring.
CSC 112L Data Structures Laboratory 1 Credit
Required lab for CSC 112.
Prerequisite: minimum grade of C in CSC 111 & CSC 111L. Corequisite: CSC 112.
Offered: every spring.
CSC 310 Information Organization and Processing 3 Credits
Databases, SQL language, concepts of normalization and database design. Rudimentary discussion of data ethics and security.
Prerequisite: a minimum grade of C in CSC 213 & CSC 213L.
Offered: once a year.
CSC 330 Operating System Design and Distributed Computing 3 Credits
The design of operating system software, including classic OS topics (scheduling, memory management, resource allocation and security) along with newer concepts. Taking CSC 253/L before this course is highly advised.
Prerequisite: minimum grade of C in CSC 213 & CSC 213L. Corequisite: CSC 330L.
Offered: every other year.
Data Science
DAT 111 Introduction to Reporting and Analysis 3 Credits
An introduction to the methods and tools for reporting quantitative data for decision support in a wide range of fields. This course is meant as an introductory course in the Data Science program, and for students in other disciplines preparing for decision support roles in a range of commercial, educational or research roles. Both the general theories and approaches to the presentation of data for decision support in tabular and graphic forms, and practical technical methods will be covered in the course. Most of the course time will be spent using Excel for these tasks, but Tableau and/or PowerBI as well as some basic SQL queries will also be covered. Whenever possible, “real-world” data drawn from a wide range of fields and disciplines will be used to illustrate problems and approaches to reporting of data.
Fulfills College Core: Field 7 (Mathematical Sciences)
Offered: every spring.
DAT 211 Advanced Statistics with R 3 Credits
This course is designed to introduce students to the programming language R. We will begin by talking about the benefits of R from a practical to an ethical level. Students will learn to install R and load packages. Students will then identify a data set they want to work with over the semester and preregister their project rationale, hypotheses, and analytic plan with OSF. Students will spend the majority of their time learning to execute their analytic plan in R. Students will present their project at Ignatian Scholarship Day. After their ISD presentation, students will archive their materials on OSF and update their preregistration to reflect any modifications made to the plan as they conducted their research, changes they would make if they were going to do the project again, and future analyses they would like to conduct with the data set.
Offered: once a year.
DAT 411 Econometrics 3 Credits
Econometrics is the science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. Econometric modeling is an important research tool in Economics, Finance, and many other academic disciplines. The goal of this course is to provide you with a basic understanding of Econometric theory and practice. We will focus on model specification, estimation, and testing, using a \hands on" approach. Throughout the course, we will use EXCEL, R, and SAS. We will cover most of Chapters 1-10 of the textbook, followed by some selected special topics as time permits. You should read through each chapter as we cover it. Special emphasis will be placed on conceptual understanding and application of econometric methods. For those who are interested in more involved discussions of the theoretical framework and/or the statistical or mathematical derivation behind any of the ideas discussed in class, feel free to meet with me outside of class.
Prerequisite: MAT 111 and CSC 111 or DAT 211 or MAT 131 or MAT 141 or ECO 255 or ECO 256.
Offered: once a year.
DAT 412 Machine Learning 3 Credits
This course introduces students to the foundational concepts and practical techniques of machine learning. Students explore both supervised and unsupervised learning methods, including linear and logistic regression, neural networks, decision trees, ensemble methods (such as random forests and XGBoost), and regularization techniques. Emphasis is placed on understanding the mathematical principles behind machine learning algorithms as well as their real-world applications. Students will gain hands-on experience using Python and libraries such as TensorFlow, applying machine learning models to structured datasets. Key topics include feature engineering, model evaluation, gradient descent optimization, overfitting, and multiclass classification.
Prerequisite: MAT 219 or MAT 450, and CSC 112.
Offered: once a year.
DAT 413 Data Stewardship and Big Data Management 3 Credits
This course introduces students to foundational and practical skills in data stewardship, with an emphasis on reproducible research and programming in R. Students will explore the data analysis process from data acquisition and cleaning to transformation, visualization, and documentation. Topics include tidy data principles, exploratory data analysis, clustering techniques, and the ethical handling of data. Students will gain hands-on experience using R and RStudio to manage large and complex datasets, utilize packages like dplyr and data.table, and produce publication-ready reports with R Markdown. The course also incorporates version control through Git and GitHub to promote collaborative and transparent workflows.
Prerequisite: no prerequisites required.
Offered: occasionally.
DAT 415 Advanced Data Visualization and Storytelling in Sports 3 Credits
This course introduces students to the principles and practices of data visualization within the context of sports analytics. Students will learn how to transform raw sports data into clear, compelling visual insights that support decision-making for coaches, analysts, and organizations. Emphasis is placed on visual perception, effective chart selection, and the fundamentals of data storytelling. Using tools such as Tableau and R, students will develop skills in data cleaning, exploration, and the creation of static and interactive visualizations. Topics include dashboard design, time-series analysis, basic geospatial mapping, and communicating insights to different audiences. Through hands-on assignments and real-world sports datasets (e.g., basketball, hockey, and Olympic data), students will apply visualization techniques to analyze performance, trends, and operational data. The course culminates in a final project where students design and present a portfolio-ready visualization that integrates analysis and storytelling. This course is designed for undergraduate students seeking practical, career-relevant skills in data visualization and sports analytics.
Offered: every fall.
DAT 417 Machine Learning for Natural Language Processing 3 Credits
This course is on constructing, training and using Machine Learning tools (neural networks) for Natural Language Processing, covering the fundamentals of operation of ChatGPT and other tools for generative language applications, translation, theme detection, text summarize, question answering and a range of other applications. This is a programming driven course, in which students will construct and evaluate a number of machine learning applications. Students will construct NLP processing models (neural networks) using the Pytorch and/or TensorFlow frameworks within the python programming language using the Jupyter notebook system. The course will also cover text encoding, tokenization, embedded and other reduced space representations, string and sentence transformations and related topics. Basic predictive models will be covered in the introduction to PyTorch and TensorFlow. Data storage in the Apache Arrow and HuggingFace datasets systems will also be discussed. Students may need to subscribe to the Google Colab Pro platform at a modest cost if they do not have regular access to a computer with an Nvidia GPU. Cost of the subscription is comparable to that of a typical electronic textbook.
Prerequisite: CSC 112 and CSC 112L.
Offered: once a year.
DAT 419 Data, AI and Ethics 3 Credits
Artificial intelligence is reshaping every profession, every institution, and every dimension of public life. This course equips students from any major with the conceptual tools and analytical frameworks needed to engage with AI as informed citizens, ethical professionals, and critical thinkers. Students explore how machine learning systems are built, trained, and deployed, gaining hands-on experience through guided modules requiring no prior technical background. Ethical, legal, and data-science-grounded frameworks are applied to real-world AI deployments, culminating in a semester-long ethics audit of a student-chosen system.
Offered: every spring.
DAT 420 Data-Driven Sport Performance: Analytics, Modeling, and Visualization 3 Credits
This course explores the use of statistical, machine learning, and AI techniques to analyze and optimize sports performance. Students will work with real-world sports data from baseball, basketball, hockey, and soccer using industry-standard tools such as R and Tableau.Topics include descriptive statistics, exploratory data analysis, regression, machine learning, AI-driven analytics, time series analysis, injury prediction, and performance optimization. Through hands-on case studies, students will learn to interpret player tracking, biomechanics, and game statistics to support data-driven decision making. The course culminates in a final project where students develop analytical solutions, create Tableau visualizations, and present actionable recommendations based on a real-world sports performance scenario.Cross-listed with DSA 520. Open to undergraduate students who have completed an introductory statistics course.
Prerequisite: DAT 413 recommended.
Offered: every spring.
DAT 425 Sports Betting Analytics: Machine Learning, AI, and Data-Driven Decision Making 3 Credits
This course explores the application of artificial intelligence, machine learning, and data analytics in sports betting, with a focus on predictive modeling, statistical analysis, and risk management. Students will analyze real-world betting data across sports such as baseball, basketball, football, soccer, tennis, and cricket using industry-standard tools including R and Tableau. Topics include probability theory, exploratory data analysis, regression, machine learning, time series analysis, in-play betting analytics, market inefficiencies, and risk management strategies. Through hands-on case studies, students will develop AI-driven models to evaluate betting outcomes and identify data-driven insights. The course culminates in a final project where students collect and analyze betting data, build predictive models, and present actionable recommendations through written analysis and Tableau visualizations. Cross-listed with DSA 525. Open to undergraduate students who have completed an introductory statistics course.
Prerequisite: DAT 413 is suggested.
Offered: every spring.
DAT 499 Independent Study Course in Data Science 1-3 Credits
Study and work with a faculty supervisor. Project to be determined by faculty agreement. Independent studies require an application and approval by the associate dean.
Prerequisite: DAT 211.
Offered: every fall & spring.
Mathematics
MAT 111 Calculus I 4 Credits
For science and mathematics majors. Calculus of functions of single variable. Functions, limits, differentiation, continuity, graphing, logarithm, exponential and inverse trigonometric functions, related rates, optimization problems, mean value theorem, l'Hospital's rule, anti-differentiation, definite integral. Credit not allowed if student already has credit for MAT 115.
Fulfills College Core: Field 7 (Mathematical Sciences)
Offered: every fall & spring.
MAT 112 Calculus II 4 Credits
Applications of integration, integration techniques, improper integrals, sequences, series, convergence tests, Taylor's series, applications; parametric and polar curves.
Prerequisite: minimum grade of C- in MAT 111 or MAT 115.
Offered: fall & spring.
MAT 211 Calculus III 4 Credits
Continuation of MAT 111 and MAT 112. Analytic geometry of 3-dimensional space and calculus of functions of several variables.
Prerequisite: minimum grade of C- in MAT 112.
Offered: every fall.
MAT 219 Linear Algebra 4 Credits
Vector spaces and inner product spaces. Linear transformations and matrices. Eigenvectors, eigenvalues, and applications. Orthogonal transformations. Quadratic forms and quadric surfaces.
Prerequisite: MAT 112 or permission of instructor.
Offered: spring.
MAT 341 Numerical Analysis 3 Credits
Study of various numerical methods and approximation algorithms. The course discusses how and why numerical methods work, as well as their errors. Topics include: How a computer does arithmetic, root finding, solving systems of equations, data fitting (interpolations, splines, regression), numerical differentiation/integration, finding eigenvalues/eigenvectors, etc.
Prerequisite: MAT 219 or MAT 450, and CSC 111 & CSC 111L.
Offered: spring of even-numbered years.
