ITAO Undergraduate Courses

Statistical Inference in Business focuses on using data to make sound inferences about a population based on sample data, especially in business contexts. More specifically, students will learn how to make inferences using test statistics and confidence intervals in contexts of multiple groups and/or multiple variables, with multiple regression and related methods heavily emphasized. Throughout the course, issues of sampling variability, research design, causality, and the assumptions and limitations of the methods are discussed. Students will supplement their conceptual understanding of the material using statistics software.

It is very important in the current age of automation and data-driven business models to have a basic understanding of coding, and to acquire some of the skills of programming. This course introduces students to Python, a widely used programming language among data scientists, with the goal of cleaning, modeling, transforming and analyzing data. Students will learn fundamentals of coding, use python packages for acquiring data from various sources, learn skills to slice and dice the data and produce data visualizations. They will gain experience in Python and apply these skills in generating reproducible reports in business contexts. In addition, students will have opportunity to apply programming skills and work on various projects/datasets that are pertinent to all the majors in the business school.

The development of data insights utilized to create a competitive advantage, optimize processes and decide on strategy is increasingly becoming more commonplace in organizations today. Software companies have “commercialized” this process and made access to information from datasets available to anyone through easy tools and interfaces, yet this has created an environment full of noise, leading to a loss of the important insights necessary to create value. This course will provide a foundation for students to develop and effectively communicate clear visual insights and actionable data necessary for defined audiences using Tableau and other visualization and presentation tools.

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.

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.

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. We’ll learn how spreadsheets (using them with powerful add-ins) can help solve these sorts of problems. In particular, we’ll learn how the techniques of simulation and optimization can help make a variety of businesses more competitive. Only a basic familiarity with spreadsheets is assumed.

It is very important in the current age of big data and data-driven business models to have basic skills of programming. This course introduces students to Python, a widely used programming language among data scientists, with the goal of cleaning, modeling, transforming and analyzing data. Students will learn fundamentals of programming, use python packages for acquiring data from various sources, learn skills to slice and dice the data and produce data visualizations. They will gain experience in Python and apply these skills in generating reproducible reports in business contexts. Also, this course prepares them for more advanced data science and machine learning methods.

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.

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.

You can find data everywhere, but that does not mean that it is always ready to analyze. Data is increasingly being saved in complicated structures, while also becoming larger. With this increasing complexity, analysts need to be able to perform any number of data manipulation tasks. In this class, we will start at the basics of R (vectors, lists, and data frames) and work our way up through iteration, functions, and visualization. Through our class activities, you will be able to more confidently and quickly work with data-related tasks that require high-level programming.

While Amazon and Dell used the internet to create new retailing business models, that same technology was instrumental in destroying the business models of the telephone and music industries. What caused the difference? We’ll examine how to use IT for competitive advantage in a digital economy. We’ll explore how IT improves problem solving, productivity, quality, customer service, and process reengineering. We’ll also examine how to apply current technologies in innovative ways to impact an organization’s bottom line.

The social media landscape is being fueled by new applications, growth of devices, and the appetite for online engagement. This engagement is what provides us with the vast amounts of data and an opportunity to better understand our society and markets. In this course we will examine user-created content from many different sources of social media, such as Twitter, MeetUp, FourSquare, and more. From within the R language we will learn to procure, process, analyze, and present our findings. Some of the methods we will utilize in this course will be sentiment analysis, topic modeling (LDA), and recommender systems. In this course we also have open discussions on current events involving social media data.

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.

Data-informed decision making has created new opportunities, but also expands the set of possible risks to organizations. One of these risks comes from grappling with the “should we?” question with regard to data and analytics, and associated concerns with identity, privacy, ownership, and reputation. In this course, we will explore several frameworks to address the issues related to the proper roles of public law, government regulation, professional codes, organizational approaches, and individual ethics in performing and managing analytics activities. The course will cover applicable theory and guidelines, and also make use of case studies. Upon completion, the student should be comfortable adapting one of these ethical frameworks for use in alignment with their organizational mission.

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.

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.)

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.

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.

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.

This course will teach you how 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.

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.

Admission to this course is *by invitation only*. Students enrolled in this course will join a capstone team for the MSBR or MSBA-SA Specialized Masters Programs. This course provides an intensive, integrative experience while working with industry partners. Students will be presented with a real business problem and have access to relevant data. They will need to develop a thorough understanding of the problem and the associated data, then develop and execute a project work plan that analyzes the data available, develops actionable recommendations, and provides insight into the basis for those recommendations. Skills developed include the ability to provide effective communication of analytics results, and an understanding of key aspects of analytics solution deployment.

Designing effective machine learning solutions has become an important topic in industry and academic research. In particular, human-centered AI – the design, development, and application of advanced machine learning and user modeling methods to human generated content including structured, text, image, and sensor-based data – has garnered considerable attention due to its immense potential to generate business value and improve the human condition. However, this value proposition also comes with a bevy of AI governance concerns attributable to the use of increasingly complex black box models. Using a combination of academic readings and real- world examples, this seminar-style course will discuss the state-of-the-art for human-centered AI. Students will be introduced to design frameworks and best practices for developing novel machine learning artifacts and measuring their utility with respect to model performance, monetary value, and humanistic outcomes.

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.

This course introduces students to many of the latest trends in the use of digital technologies and examines the ways that companies use technologies strategically. We examine how technology developments have altered the business landscape to enable new processes, products, and business models. We study a variety of real-life companies that invest in technological innovations to gain a competitive edge. We discuss their innovation processes and analyze their strategies.

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.

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.

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.

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.

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.

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.

Data-informed decision making has created new opportunities, but also expands the set of possible risks to organizations. One of these risks comes from grappling with the “should we?” question with regard to data and analytics, and associated concerns with identity, privacy, ownership, and reputation. In this course, we will explore several frameworks to address the issues related to the proper roles of public law, government regulation, professional codes, organizational approaches, and individual ethics in performing and managing analytics activities. The course will cover applicable theory and guidelines, and also make use of case studies. Upon completion, the student should be comfortable adapting one of these ethical frameworks for use in alignment with their organizational mission.

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.

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.

This course provides an introduction to the emergent field of social studies of data-intensive analytics for the examination of how “things are done with data.” The goal is to cover a wide range of examples and practical applications to introduce questions of design and implementation, privacy and surveillance, as well as governance and stewardship of digital tools and infrastructures. Following the performative aspect of data, we will explore social, technical, political, and economic dynamics that involve data extraction, sharing, literacy, and analysis. From little to big data practices, we will examine at the interface level the professional and institutional applications, development histories, and current political economy of data to situate ourselves as engaged technologists and researchers, not detached critics or passive users. There are no prerequisites for this course: no previous experience in statistics or programming is needed, but independent study of the supplementary materials we provide is highly encouraged.