AI Analytics in Business Minor

Course Number: ITAO 30100
Credit Hours: 3

Business Analytics allows us to make sense of what we see in the real world by using data and a systematic approach to solve real problems and make business decisions. This course provides the fundamental concepts and methods needed to understand the emerging role of business analytics in organizations. You will learn how to properly plan an analytics strategy, collect data, analyze the data and report findings through visualizations and storytelling. Having a strong understanding of concepts in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success in the remainder of the Business Analytics major and beyond.

Course Number: ITAO 30170
Credit Hours: 3

This course equips students with practical skills for working with generative AI in modern academic and business settings. Students learn how large language models work and how to use them thoughtfully for data analysis, modeling, research, and communication. The course emphasizes using AI to improve insight and clarity while maintaining responsibility and independent judgment. It is designed for students who want to be prepared, not surprised, by how AI is reshaping business work.

Department: Accountancy
Course Number: ACCT 30280
Credit Hours: 3

This course applies data analytics to settings within accounting, using statistics as the primary method and Microsoft Excel as the primary tool along with other add-ins and programs. The aim of the course is to enhance a student’s ability to think systematically about data, structure it into a usable and interpretable form, create decision models, and weigh probability, risk, trade offs, and the limitations of data.

Department: Accountancy
Course Number: ACCT 40280 
Credit Hours: 3

This course will advance your skills in the analytical tools most commonly used in current accounting and financial consulting practices, and will include applications in regulatory compliance, financial management and reporting, investment analysis, forecasting, valuation and simulations, operations management, forensics, and financial analysis. You will use advanced Excel techniques in forensic and financial analytics, as well as extraction, transformation, visualization, regression, natural language processing, and data management tools in Tableau, R, Alteryx, and Python. These analytical skills will allow you to identify areas where you can add value by identifying anomalies with respect to fraud, internal controls, or key financial, audit risk, and/or tax positions and the associated risks or opportunities for clients and stakeholders. Moreover, you will have confidence in the topics incorporated into the new CPA Evolution 2024 for applied technology, including: relational databases, normalization of data, structured and unstructured data, data extraction and transformation, forecasting to model financial results, cybersecurity, sensitivity analysis, and predictive analytics.

Course Number: ACCT 40850
Credit Hours: 1.5

This one-and-a-half credit hour course will explore issues, ideas, and trends related to artificial intelligence and accounting A series of separate lectures on Friday afternoons will feature a wide range of experts on issues related to Accounting and Finance areas including auditing, taxation, fraud, mergers and acquisitions, transactions services, and investment banking.

Course Number: ACMS 40875
Credit Hours: 3

Course Number: ACMS 40876
Credit Hours: 3

Given the growing volume and complexities of real-world data, successful deployment of data science pipelines into practice often require intangible factors, beyond the modeling, including careful formulation of the substantive problem of interest, non-trivial data pre-processing, powerful computational software, intuition into when and why models work or fail to work, effective visualization and communication of the results, awareness of the ethical consequences, and close collaborative efforts. Data Science in Practice: Tools and Applications explores these computational and critical thinking skills necessary to solve data science problems in real-world applications. To this end, this course will guide students through a series of hands-on learning projects based on real scientific datasets. Through these real-data projects, students will gain experience with data pre-processing, advanced visualization tools, unsupervised and supervised learning tasks, interpretability tools, and advanced computing tools that are commonly used in industry such as git, distributed computing, reproducible documentation, and open-source software packaging.

Course Number: AME 40411
Credit Hours: 3

This course offers a comprehensive exploration of Artificial Intelligence (AI), covering topics ranging from generative modeling to Chat-GPT. The curriculum begins with an examination of the core concepts of deep learning, followed by practical, hands-on experience implementing Linear Deep Learning and Multi-Layer Perceptrons using Python and PyTorch. The latter part of the course is dedicated to in-depth study cases and projects, delving into Generative Modeling, Convolutional Neural Networks (ConvNets), Unsupervised and Self-Supervised Learning, Natural Language Processing, and Reinforcement Learning.

Course Number: ANTH 20110
Credit Hours: 3

“Hacking” is one of the most pressing topics of technological and societal interest. Yet, it is one of the most misunderstood and mischaracterized practices in the public sphere, given its ethical and technical complexities. In this course we will combine anthropological and computer science methods to explore the digital tools, practices, and sociocultural histories of hacking with a focus on their context of occurrence from the late 1960s to the present. Our goal is to help students think anthropologically about computing as well as technically about the digital mediations that we depend on in our lives. Computer Science is a great partner for Anthropology: computational methods have been part of the discipline since the 1970s with the use of digital computers for the study of cultural phenomena. Likewise, archaeology is a powerful companion of computing: its approach to material culture is suitable for the study of the sociocultural aspects of tool-making that are fundamental for understanding computing expertise. One of the most important aspects of hacking today consists in creating a community around tools for the exploration of digital technologies. The interplay between social and technical aspects, however, is one of the most neglected in the existing literature on hacking and computer security. Our proposal is, therefore, to bridge a serious gap between social and technical studies of digital technologies with a focus on hacking.

Course Number: ANTH 40707
Credit Hours: 3

Course Number: CDT 30750
Credit Hours: 3

Generative AI is a form of computing in which computer systems generate media of text, images, sound, video, or combinations based on prompts or other information provided to the computer. These systems, including, but not limited to, ChatGPT, Midjourney & DALLE, have been evolving rapidly and have led to extreme excitement, confusion, and fear. This course provides a survey of how to understand and use a number of these tools including explorations in prompt engineering. The class will address a range of issues from across the liberal arts including artistic, economic, social/psychological, environmental, educational, and legal concerns and opportunities.

Course Number: FIN 40260
Credit Hours: 3

This course is intended to provide Finance majors with a working knowledge of the open source programming language Python. The course will teach the essential aspects of coding in Python and then apply the tool to financial applications involving analytics, large datasets, and unstructured data. The objective of the course is to provide students with a better understanding of how computers can be used to solve business problems. Students will be required to bring their own computer to class.

Course Number: FIN 40262
Credit Hours: 3

With increased data availability and the rapid advancement of artificial intelligence, finance professionals need both strong data analysis skills and the ability to leverage modern AI tools effectively. This course introduces students to AI and data applications in finance using the Python programming language and its ecosystem of packages. The course is structured in four main parts. First, you will build a solid foundation in Python programming for finance, learning to work with NumPy and Pandas to collect, analyze, and visualize financial and macroeconomic data. Second, you will be introduced to AI-assisted coding tools (such as Claude, ChatGPT and similar AI coding assistants) that help you write better code, understand complex codebases, and accelerate your workflow. You’ll learn how to use AI tools to make your work more efficient and rigorous. Third, you will learn machine learning techniques for finance, including dimensionality reduction with Principal Component Analysis (PCA) and penalized regression methods (LASSO, Ridge) to make predictions and identify patterns in financial data while avoiding common pitfalls like overfitting. Fourth, you will gain exposure to large language models (LLMs) and natural language processing, learning how to apply these tools to analyze financial text, classify sentiment, and build retrieval-augmented generation (RAG) systems for finance applications. Throughout the course, you will work on practical finance applications: characterizing security price movements, evaluating portfolios, predicting stock returns, and analyzing financial texts. As the field of artificial intelligence continues to grow in finance, possessing an understanding of how to leverage AI tools effectively becomes increasingly valuable for finance professionals. This course provides a practical introduction to these tools within a finance context. In the honors version of the course, students will engage more deeply with the research frontier of AI, machine learning, and text analysis in finance, including a small group project replicating the core analysis of a published finance and machine learning paper using modern computational methods in Python.F

Course Number: ITAO 30220
Credit Hours: 3

The unprecedented availability of data and information now allows companies to rely on facts rather than intuition to drive their business decisions. Giant online retailers like Amazon.com investigate customers’ browsing histories to recommend products that may be of interest to customers. Banks study the payment patterns of old customers to predict the likelihood that new borrowers will default. Wireless providers analyze usage data to predict customer turnover. Firms can make better strategic and tactical decisions and gain competitive advantages by leveraging the tremendous amount of data now available on the table. We’ll study the tools and techniques these companies and others use to make better and faster decisions, and we’ll learn about how methods such as data mining can be used to extract knowledge from data.

Course Number: ITAO 30230
Credit Hours: 1.5

Relational databases store the majority of the information used in business analytics efforts and data analysts work with these crucial infrastructure platforms on a daily basis. In this course, you will gain an understanding of the key concepts surrounding the storage and security of structured data in relational databases. You will learn how to create, modify and query databases using the Structured Query Language (SQL). You will also discover how data analysts clean and transform this data into forms suitable for analysis using the R programming language. Finally, you will gain an understanding of the issues surrounding Big Data applications and the use of unstructured data in business analytics efforts.

Course Number: ITAO 30620
Credit Hours: 1.5

Digital disruption is reshaping entire industries in today’s global economy. In this dynamic environment, organizations must be agile and innovate with emerging technologies to generate new value propositions. Using the concept of “macro technology forces,” we will explore how the past, present, and future of IT innovation tends to follow the same three-tiered architecture over the past 160 years: Computation, Information, and Interaction. This course will provide you with a frame of reference, or lens, to apply to business problems that will help you think about ways that firms can be digitally transformed.

Course Number: ITAO 30660
Credit Hours: 3

Whether you become a high-profile real estate developer, an investment banker, or an entrepreneur, in any career you’ll need some project management skills to get your job done. Everyone tries to get projects finished on time and under budget, but many critical business projects fail anyway. We’ll learn the steps associated with successful project management, examine some optimization techniques, learn how to use the software tools that enhance productivity, and discuss how to avoid the implementation pitfalls that cause good people doing good projects to fail.

Course Number: ITAO 30810
Credit Hours: 3

Business and government leaders are increasingly recognizing the importance of involving the whole organization in making strategic decisions in order to compete globally. Because an organization usually commits a bulk of its human and financial assets to operations, operations is an important function in meeting global competition. Firms today compete on multiple dimensions and must align their operations to whatever competitive choices they make. Successful firms have demonstrated that operations can be an effective competitive weapon and, in conjunction with well-conceived marketing and financial plans, these firms have made major penetrations into markets worldwide. This course is designed to address key operations and supply chain management issues in a variety of contexts including manufacturing and service organizations as well as for-profit and non-profit organizations. Students will understand the role of operations in the overall business strategy of the firm and be able to identify and evaluate the key factors in the design of effective operations for the production of products and delivery of services. The course also covers a range of tools appropriate for the analysis of a firm’s operations and offers an opportunity to discuss and compare various approaches to operations and supply chain management in an international context. The aim of this course is to (1) familiarize students with operations and supply chain management, and (2) provide students with language, concepts, insights and tools to solve operational issues in order to gain competitive advantage through more efficient and effective processes.

Course Number: ITAO 40230
Credit Hours: 1.5

This course aims to help students develop advanced data analytics and AI skills to tackle real-world business challenges. Using Python, students will learn to clean, transform, and analyze large datasets to uncover meaningful patterns and translate them into business intelligence through insightful visualizations. Students will also learn the essential machine-learning techniques to forecast trends and make predictions. Additionally, students will learn how to build AI agents to automate workflows and enhance decision-making. Emphasizing practical applications, this course will equip students with extensive hands-on experience in the most popular Python libraries including NumPy, Pandas, Matplotlib, Seaborn, scikit-learn as well as modern frameworks for building AI agents such as LangChain, AutoGen, and CrewAI. At the completion of the course, students will be able to leverage data analytics and AI skills to help businesses enhance decision-making, optimize performance, and create competitive advantage. Students are strongly encouraged to complete ITAO 30220 before enrolling in this course.

Course Number: ITAO 40250
Credit Hours: 1.5

Approximately 80% of the world’s data is unstructured, that is data that does not conform to relational database principles. It is growing at fifteen times the rate of structured data. Unstructured data includes corporate e-mails, financial filings, customer feedback, blogs, online reviews, instant messages, tweets, pictures, videos, and graphs among others. Extraction of insights from unstructured data is increasingly viewed as a high-valued opportunity but is still a nascent area within many companies and other organizations. Analytic techniques are increasingly important for understanding what can be learned from unstructured data sets and demand is strong for unstructured data analytical skills.

This course introduces students to the process of performing high-valued analytics with unstructured and semi-structured data to support business decisions. Students will identify relevant data sources (big and small), learn how to use contemporary technologies such as the Hadoop ecosystem to store and process the data, implement advanced processing and analytical techniques, and develop predictive models. The course will introduce and use concepts in machine learning, natural language processing and information retrieval to solve real-world problems.

Course Number: ITAO 40420
Credit Hours: 1.5

Machine learning is the science of getting technology systems to act without following prescriptive software. Most AI is unknowingly used daily by humans in their cars, homes, companies and experience it in the infrastructure of our nation. Most think we are in the midst of a new industrial revolution that is driven by AI software accompanied by sensors and big data that feed the software what it needs to act. This course will teach machine learning techniques and the application of those techniques. The course will cover supervised learning, unsupervised learning, best practices and AI safety or the ethics of AI. The course will examine real life examples such as robotic control, text understanding, medical informatics, and many other areas being impacted by machine learning.

Course Number: ITAO 40510
Credit Hours: 1.5

Artificial intelligence and data-driven decision-making have afforded unprecedented opportunities for value creation, while also introducing a complex set of risks for organizations and society. These risks extend beyond technical performance to how data and AI systems are designed, deployed, and governed—and to concerns about agency, dignity, accountability, privacy, trust, and safety. Grounded in foundational ethical inquiries, the course equips students to analyze complex, ethically inflected AI decisions in the light of individual actions, organizational challenges, social and regulatory environments, and operational realities. Students examine the ethics of data creation, collection, storage, use, and transfer in a landscape increasingly dominated by AI-powered algorithms.

Course Number: ITAO 40515
Credit Hours: 3

As artificial intelligence (AI) grows increasingly pervasive in society, it is essential that we develop an understanding of how AI systems work. A vital part of this understanding is a careful consideration of various risks (e.g., the presence of bias, a lack of transparency, regulatory compliance) when AI systems are designed and deployed in real-world settings. To understand and address these concerns, this course introduces students to the fundamentals of AI auditing ? the practice of evaluating and improving the ethics of AI systems. Through a combination of interactive discussions and semi-technical lab sessions, students will develop an auditing “toolkit.” This toolkit includes both theoretical and technical concepts, especially relevant for the increasingly interdisciplinary teams of the modern workforce. Students will work on group case assignments as “audit committees” that reflect the needs of a variety of stakeholders (e.g., developers, managers, investors, users). Groups will identify and discuss potential concerns or risks associated with AI systems as well as develop recommendations to address them. Overall, the course aims to provide an interdisciplinary and hands-on introduction to AI auditing, allowing students to gain insights into the opportunities and challenges associated with the design and deployment of AI systems that minimize societal risk and increase their effectiveness.

Course Number: ITAO 40520
Credit Hours: 1.5

Many industries are being created and transformed by using the techniques of business analytics. With the goal of studying these techniques in some depth, this course focuses on one such industry: sports. This industry has clearly benefited from the application of a wide variety of analytics techniques and has the advantage of being widely and closely followed, with large amounts of easily-accessible real-world data. Topics for study in this course include how to evaluate players, rate teams, schedule leagues, and enhance coaching strategies. Assignments involve the hands-on use of a variety of techniques and tools, which are useful in most industries. Techniques and tools include data manipulation, probability, statistics, optimization, spreadsheets, and a powerful statistics package. A basic knowledge of Excel, statistics, and sports (in particular, baseball, basketball, and football) is assumed. (You do not have to be a sports fanatic.)

Course Number: ITAO 40530
Credit Hours: 1.5

In recent years, the quantity of data available to sports teams and professional athletes has expanded significantly, it is now possible to extract detailed information about training sessions, games, and a range of the field metrics for elite athletes. This has led to the development of the field of human performance optimization. In this class we will learn how to extract insights from a range of data sources with the objective of maximizing athlete performance in competition. This includes optimizing physical readiness and avoiding injuries, long term player development and the identification of strategic advantages in competition which can be targeted by both athletes and coaches. We will use the R-coding language to develop pipelines for the analysis of the latest data sources. It is recommended that students have taken Machine Learning (ITAO 40420) before taking this class.

Course Number: ITAO 40550
Credit Hours: 1.5

Broadly speaking, social networks are the patterns of relationships between actors. As actors in these systems are not independent, each actor influences the behaviors of others in the network. Our connections to others can determine a great many aspects of our lives, including whether or not we are employed, our happiness, and even our weight status. In this course, we will cover a variety of substantive areas in which networks can influence social life, including political behavior, innovation, inequality, power, and antagonism. Students in this course will explore the theory of network structure and function, understand how networks affect our lives and organizations, and will learn basic techniques for analyzing social network data. At the end of this class, students will have the knowledge and tools required to explore their own interests within the application of social networks.

Course Number: ITAO 40570
Credit Hours: 3

Urban regions will experience most future population growth, bringing opportunities and challenges. At the same time, statistical/machine learning has been evolving rapidly in the era of big data and provides tools to inform both data-driven decision-making and long-term planning in complex urban systems. Focusing on methodologies with statistical reasoning, the course brings in a large set of cutting-edge machine learning techniques combined with up-to-date urban case studies. We will start with data science essentials starting from data acquisition, exploratory data analysis (EDA), and visualization along with tools for reproducible reports. We next show how to build and interpret basic models; then we go beyond and focus on contemporary methods and techniques for handling large and complex urban data. By the end of the semester, students will master popular modern statistical methods, but also get equipped with hands-on skills in urban data analytics.

Course Number: MARK 30120
Credit Hours: 3

A study of the application of scientific method to the definition and solution of marketing problems with attention to research design, sampling theory, methods of data collection and the use of statistical techniques in the data analysis.

Department: Marketing
Course Number: MARK 30130
Credit Hours: 3

Marketing is an increasingly analytical profession driven by the availability of data and analytical techniques to improve decision making. This undergraduate course will introduce decision models that rely on financial data, other marketing metrics including web based key performance indicators, as well as statistical analyses. This course seeks to integrate the various analytical techniques taught in the business school within a marketing context. This course is appropriate for individuals considering careers in brand management, product management, retail management, marketing research, or consulting.

Course Number: MARK 30440
Credit Hours: 3

This digital revolution has prompted marketers to rethink their digital strategies and leverage data to optimally ‘engage’ their audiences on these contemporary platforms. In keeping with this trend, this course will help students build job-ready skills for measuring, evaluating, and responding to digital engagement using mathematical and data-driven methods. Students will learn to frame marketing questions quantitatively and use spreadsheets and Stata to build, estimate, and interpret models that inform marketing strategy. Some math and statistics background (e.g., algebra, calculus, and probability) will be assumed and is essential for the successful completion of this course.

Department: Marketing
Course Number: MARK 40150
Credit Hours: 3

This course will teach you how marketing managers make decisions about pricing and distribution, using data. We begin with understanding pricing and promoting to an individual customer, and use this foundation as we move to more aggregate decisions, such as setting regular and promoted prices at the product level and managing category pricing. A key part of the class is understanding the limitations of different types of data and how better planning can both simplify the analysis and increase your confidence in the findings. This class is designed to be very practical and hands-on. A working knowledge of statistics (e.g., t-test and regression analysis) is required and you will learn R for the analysis.

Course Number: MARK 40450
Credit Hours: 3

The overarching principles of this course are: 1) estimating customer lifetime value (CLV) and managing customers based on their CLV, 2) deciding which customers to acquire and how to acquire them, 3) clarifying the importance of customer retention and developing ideas to increase retention, 4) expanding existing customer relationships and increasing share of wallet through cross-selling and up-selling, and 5) linking CLV to organizational-level concepts such as firm valuation. The course requirements and format include lectures, case analyses, simulations, student-led discussions, and short papers.

Course Number: MATH 20580
Credit Hours: 3

An introduction to linear algebra and to first-and second-order differential equations. Topics include elementary matrices, LU factorization, QR factorization, the matrix of a linear transformation, change of basis, eigenvalues and eigenvectors, solving first-order differential equations and second-order linear differential equations, and initial value problems. This course is part of a two-course sequence that continues with Math 30650. Credit is not given for both Math 20580 and Math 20610.

Department: Management & Organization
Course Number: MGTO 30633
Credit Hours: 3

Anita Roddick built the Body Shop on the stories behind her products. Walt Disney told stories that created an immersive experience in magical worlds. Richard Bronson never shied away from telling an unpolished story, knowing that flaws make a story memorable. Among the greatest storytellers of all time, these three understood the power of storytelling to inspire others, drive decision-making, and ignite action, particularly when combined with data and sound logic. This course teaches students the art of clear, effective, and engaging data presentation using storytelling techniques. Delivered in a hands-on, workshop-style format, students build a data story from start to finish focusing on context, visual design, strategic messaging, and persuasive delivery.

Course Number: PSY 30109
Credit Hours: 3

This class aims to equip students with basic knowledge of R in data manipulation, data generation, data visualization and data analysis with a focus on data science. The first part of the class will introduce the very basics of R including the types of data such as vectors, matrices, and data frames as well as tibbles for refined data frames and bigmatrix for big data. The second part of the class will introduce data manipulation and preprocessing methods such as data transformation, subsetting, and combination. The third part will deal with specific types of data such as strings, texts, dates and times, images, audios, and videos. The fourth part will teach ggplot2 and related packages for data visualization. The last part of the class will illustrate how to conduct data analysis using the above techniques through case studies such as basket analysis, network analysis, and log analysis. The class does not require previous knowledge of R

Course Number: PSY 40122
Credit Hours: 3

Machine learning is becoming an important tool in psychology, offering new ways to analyze data, make predictions, and uncover patterns that may be difficult to detect with more traditional methods. This undergraduate course introduces the core ideas behind machine learning and their application to psychological research. Students will learn how prediction-focused methods can complement hypothesis-driven approaches by helping researchers evaluate models, classify outcomes, and explore complex relationships in data. Topics may include regression and classification, cross-validation, regularization, decision trees, random forests, k-nearest neighbors. The course emphasizes hands-on learning, clear interpretation, and thoughtful use of machine learning in psychological and behavioral science.