Data Analytics -DAT

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.