Business Technology and Analytics 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 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 40730
Credit Hours: 3

Cloud computing is a transformative force in the development of technology solutions that meet business requirements. Firms no longer need to make significant capital investments in large-scale data centers that sit idle for extended periods of time. The cloud model offers flexible, scalable, and cost-efficient access to computing resources on a just-in-time basis. In this course, we will explore the applications of cloud computing to common business problems. We will explore full-stack cloud solutions, including the use of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) technologies.

Course Number: ITAO 40450
Credit Hours: 1.5

There is no shortage of data in the world. While data is constantly swirling around us, actually acquiring it can prove difficult. To make actionable insights, we need a way to collect that data and prepare it for analysis. In this course, you will learn how to acquire data through several methods: application programming interfaces (APIs), web scraping, web-based surveys, and streaming. We will take an in-depth look at each of these technologies, so that you can apply them in any real analytics scenario. You will be able to use these various technologies to collect data and conduct analyses commonly seen for each type of method (from standard modeling techniques to factor analysis and beyond). This course will focus on using R, Python, and Qualtrics to collect and analyze data, in addition to exploring modern data streaming technologies. While Qualtrics will be used for survey creation and administration, the survey methodology skills will translate to any survey program.

Course Number: ITAO 40217
Credit Hours: 3

Digital devices and communications are a part of daily life. From computers to cell phones to online accounts, we generate a significant digital footprint. As such, most civil and criminal investigations contain a nexus to digital evidence. This course will cover the principles of digital forensic analysis, including Electronic Discovery and the forensic process of Extraction, Processing, and Analysis. Students will learn and develop skills related to: acquiring smartphone, computer, removable media, and other forensic images; analyzing artifacts, file systems, and registry data; use of multiple methods and verification features to validate findings; and how to generate reports and distribute findings to share digital forensic results quickly and easily. Students will have the opportunity to use commercial digital forensics software to participate in hands-on lectures and practical exercise. This will include conducting digital forensic analysis on a computer, an iOS device, an Android device, and multiple items from cloud accounts. At the conclusion of the course, students will have a firm base knowledge of digital forensics and be able to independently perform digital forensics exams.

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 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 40440
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: ITAO 30640
Credit Hours: 3

In today’s digital age, people and organizations produce and deal with unprecedented amounts of data. Thus, issues concerning information privacy and security have taken on critical importance. Information privacy and security are fundamentally about data protection. Information privacy refers to decisions around what information should be protected, from whom, why, and issues related to the ownership of information; whereas information security refers to the tactics and technologies to ensure data protection. In this course, we will address questions such as: How should organizations manage privacy and security issues? What are the various privacy and security threats that organizations and individuals face? What are the current advancements in privacy and security technologies and government regulations? We will learn about economics of privacy, biases and heuristics in privacy decisions, privacy ethics, social engineering, and public policy and regulations. Also, we will gain an understanding of security threats and gain insight into managerial best practices for managing information security. This course will involve a number of assignments along with interactive in-class exercises aimed at enhancing your privacy and security decisions.

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 30630
Credit Hours: 3

Each day, organizations like Wal-Mart analyze hundreds of millions of transactions to increase efficiency and better serve their customers. We’ll use market-leading Oracle Enterprise Database software to store and analyze large datasets just like Wal-Mart does. In addition, you’ll serve as an IT consultant and build a real-world application for a client organization. In this role, you’ll experience the entire system analysis process, including problem definition & analysis, design processes, testing, and implementation.

Course Number: ITAO 30670
Credit Hours: 3

The production and business of video games has grown to become a $200+ billion industry and a leading form of entertainment globally. Video games attract players from diverse backgrounds and the mainstream consumer base is no longer limited to “hardcore gamers.” Games have garnered mass appeal on social and casual platforms, PCs and consoles, smartphones, virtual reality, in competitive eSports tournaments, and more. Weekly topics of study in this course include game design/psychology, gaming technologies, artistic fundamentals, development workflows, business models and structures, funding and financing, marketing, distribution channels and markets, and legal/ethical issues. Through lectures, discussions, group exercises, reading assignments, and gameplay sessions, students will gain a thorough understanding of this highly creative and complex industry. Furthermore, students will gain an appreciation for video games as a legitimate form of art and a significant force for cultural impact.

Course Number: ITAO 40150
Credit Hours: 1.5

Whether it is picking an investment portfolio, moving goods through a supply chain, staffing a customer support center, or deciding how many reservations an airline or hotel should take, business decisions involve substantial quantitative analysis. In this course, you will learn how simulation and optimization can help solve such problems, while also learning how these techniques are applied across other domains like statistics and machine learning. Most importantly, you will learn how to implement these techniques through programming languages like R and Python.

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.

Department: Applied and Computational Mathematics and Statistics
Course Number: ACMS 40875
Credit Hours: 3

Data mining is widely used to discover useful patterns and relationships in data. We will emphasize on large complex datasets such as those in very large databases or web-based mining. The topics will include data visualization, decision trees, association rules, clustering, case based methods, etc.

Department: Anthropology
Course Number: ANTH 40707 
Credit Hours: 3

This course provides an intensive introduction to statistical methods of use for anthropological research. It will examine why and when to use quantitative methods, and how such methods can be incorporated into a holistic anthropological research design. Topics covered include probability theory, and parametric, non-parametric, and Bayesian principles of hypothesis testing, data ordination, and methods of analyzing non-independent data including network analysis. All course work will be undertaken using free statistical packages available through the R programing language. No prior mathematical or programming experience is needed.

Department: Economics
Course Number: ECON 40410
Credit Hours: 3

The new wave of technologies, e.g., robotics and AI will have long-lasting impacts on the labor market. Jobs will be displaced, new tasks will be created, different skills will be demanded, and new management practices will emerge. These new technologies may benefit workers unevenly, potentially increasing inequality. At the same time, new demographic challenges driven by aging will have large impacts on labor. How will these forces affect the future of labor and how should we prepare for changes in the labor market? The goal of this course is to provide students with a framework for analyzing how new technologies like robotics and AI will affect the labor market drawing largely from the economics literature. Students will analyze and describe the literature on these topics and understand the different methodologies used in the literature. Ultimately, students will build perspectives on how AI and robotics could affect jobs, occupations, the future of work, income distribution and social institutions. Students will also build perspectives on education, training, and redistribution policies that can help mitigate the labor market disruptions created by technological change. Students will collect and analyze data that can provide insights on the future of labor.

Department: Finance
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.

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.

Department: Marketing
Course Number: MARK 30230
Credit Hours: 3

A significant and growing trend in the marketing profession is the use of mathematical and statistical models to inform managerial decision making. In this class, students will learn to use Microsoft Excel, STATA and Calculus to model real-world marketing problems.

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.

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.

Department: Psychology
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