Courses
BANA 310. Data Visualization for Business (3)
Prerequisite: SOM 120 or MATH 140 (Business Analytics majors and minors must attain a grade of “C” or higher). (MATH 140 is cross-listed with MATH 140BUS, MATH 140SCI, and MATH 141.) This course covers the concepts and methods in data mining and analysis relating to exploration and visualization of business data, leading to knowledge discovery from large data sets and better managerial decision making. This course examines a comprehensive set of ways useful in understanding and presenting data using the latest data visualization software and tools. Major topics of this course will include summarizing data using tables and plots. Students will learn through a combination of lectures and hands-on short case studies. Ethics is one of the integral parts of this course since charts and tables can lead the users to a particular direction and action that the analyst wants. Students will learn how to responsibly visualize data.
BANA 320. Predictive Analytics for Business (3)
Prerequisite: SOM 307 (Business Analytics majors must attain a grade of “C” or higher). An introduction to some of the most widely used predictive modeling techniques and relevant core principles. This course covers a solid foundation of predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions based on data. Students will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modeling, one of the essential skills valued in the business.
BANA 410. Machine Learning for Business (3)
Prerequisite: SOM 307 (Business Analytics majors must attain a grade of “C” or higher). BUS 312 and BUS 302L are prerequisites for Business majors. This course covers machine learning approaches to discover patterns hidden in large datasets. What can we predict about the future, given the data from the past? What kind of models can we construct with this information, and how can we assess these models’ behavior and reliability? Finally, how can we represent the data and devise tools for discovering these models efficiently and effectively? This course focuses on tools for data analytics. Furthermore, it covers the mathematical formulations underlying statistical processing, and the development and analysis of learning algorithms.
BANA 420. Prescriptive Analytics for Business (3)
Prerequisite: SOM 307 (Business Analytics majors must attain a grade of “C” or higher). BUS 312 and BUS 302L are prerequisites for Business majors. Prescriptive analytics uses both descriptive analytics and predictive analytics as input to models that prescribe preferred courses of action or decisions. Prescriptive analytics answers the question, what should happen? BANA 420 is a modeling course. Students will learn how to develop mathematical models in business, and especially in operations management and strategy, financial management, and marketing management, the three key functions of all business systems. The primary focus is on model building and applications. Solution procedures have a secondary role. Students also learn how to interpret the solutions and effectively communicate the results. The course will focus on understanding prescriptive models and how to implement or embed them in organizations and the decision-making process.
BANA 430. Text Mining and Analytics for Business (3)
Prerequisite: SOM 307 (Business Analytics majors must attain a grade of “C” or higher). BUS 312 and BUS 302L are prerequisites for Business majors. In addition to books and news, the general public creates considerable content on social media platforms and applications as well as other websites expressing product reviews. Advances in text mining and social media analytics now play an important role in today’s business decision making. This course will introduce concepts and methods used in text mining and analytics to drive information from text. Students will learn how to process unstructured data, such as information that is not organized in a uniform format, images, videos, and audio. These types of data are heterogeneous, non-compliant with standard schema and often high-dimensional making pattern discovery challenging. Students will learn how to use natural language processing to transform the unstructured text in documents and databases into normalized, structured data suitable for analysis. Students will learn through a combination of lectures and hands-on examples using R and Python.
BANA 440. Supply Chain Analytics (3)
Prerequisite: SOM 307 (Business Analytics majors must attain a grade of “C” or higher). BUS 312 and BUS 302L are prerequisites for Business majors. Corequisites: BANA 320 and BANA 420. Managing supply chains is a complex and challenging task, given the current business trends of expanding product variety, short product life cycles, increasing outsourcing, globalization of business, and continuous advances in information technology. Many companies are aggressively investing in data analytics tools to generate insights that can help them make well-informed business decisions. This course is offered to help students understand the analytical tools and techniques that are useful in designing and managing supply chains. The emphasis will be on how the models can be used in some fundamental supply chain applications such as transportation, capacity allocation, production planning, network flow, aggregate planning, sales and operations planning, and network design.
BANA 607. Introduction to Business Analytics (3)
Prerequisite: SOM 591 or equivalent. This course introduces the methods and tools which help to systematically extract not only information but also insights from the data in various business functions, such as operations, supply chain marketing, and finance. The course consists of four distinct parts: (1) foundations of business analytics: decision making, definition and categories of business analytics, big data, (2) descriptive analytics: descriptive statistics, data visualization, statistical inference, (3) predictive analytics: regression, time series analysis, forecasting, data mining, spreadsheet modeling, and (4) prescriptive analytics: linear optimization, integer optimization, simulation, decision analysis.
BANA 610. Visualization and Communication for Business (3)
Prerequisite: Graduate standing. This course helps students link the various facets of business and provides them with an intuitive way to better understand data. Data visualization consists of tools and techniques to generate insights from data and convey them to others. It is also essential in identifying data errors and reducing the size of data by highlighting important relationships and trends. More specifically, in this course, students learn how to design tables and charts. Furthermore, it presents an overview of more advanced charts, tables, and data dashboards. Students learn how to work with state-of-the-art visualization computer software packages. The course puts an emphasis on how to responsibly visualize data and communicate findings with others.
BANA 614. Database Management for Business (3)
Prerequisite: Graduate standing. The primary goal of this course is to understand principles and practices of database management and design. Students learn the principles of database design, implementation, as well as data querying and processing. Database administration (including security, concurrency control, performance monitoring and tuning, resource sharing, and recovery) is also covered. Students learn and practice the fundamentals of the Structured Query Language (SQL), entity-relationship diagrams, dependencies and normalization. The emerging issues of data governance and related risk management are also studied. Projects require students to design and develop a database application for a business situation by utilizing design tools and a major database management systems (DBMS) package.
BANA 620. Data Mining and Predictive Analytics for Business (3)
Prerequisite: BANA 607. This course provides a comprehensive coverage of the most widely used classical and modern predictive business modeling techniques, such as logistic regression, k-nearest neighbor, naïve Bayes, clustering, neural network, regularization, etc. This course also provides a strong theoretical foundation of predictive analytics in making both classifications and predictions based on big data. Using realistic business examples, students learn how to implement the solutions using state of the art business analytics software.
BANA 622. Programming for Business Analytics (3)
Prerequisite: BANA 607. This course focuses on the state-of-the-art data science programming languages. Students learn to build algorithms and apply programming languages to discover patterns hidden in datasets. The course enables students to use interdisciplinary machine learning techniques to create algorithms, which can efficiently manage large volumes of data to quickly make real time decisions. It also covers a broad cross-section of programming techniques and prepares students in the application of machine learning techniques. This course emphasizes on building tools using programming languages.
BANA 630. Prescriptive Analytics for Business (3)
Prerequisite: BANA 607. This course aims to enhance students’ ability to make actionable data-driven decisions using optimization modeling techniques to achieve business goals. It will train students how to transform data into action using modeling with logical thinking and ultimately help them develop critical thinking in decision makings. Students will learn how to ask the right questions, define business objectives, understand and capture constraints, and optimize for success of their business. The emphasis will be on the formulation of different optimization problems and the use of the appropriate quantitative techniques, including heuristics and optimization. The course will also examine a variety of practical business analytics applications, including financial planning, human resources, marketing strategies, logistics, production planning, inventory management, and revenue maximization.
BANA 640. Supply Chain Analytics (3)
Prerequisite: BANA 607. Corequisites: BANA 620 and BANA 630. This course explores predictive and prescriptive modeling and analytical tools for design, analysis, execution and integration of supply chains. Students will learn how to formulate and solve supply chain problems using business analytics techniques. The emphasis will be on how these models can be used in fundamental supply chain applications, such as transportation, forecasting, capacity allocation, production planning, sales and operations planning, facility location and distribution network design. This course also provides an example-driven approach to learn about important supply chain models, problems, and solution methodologies. This course is designed to help students master the analytical tools and techniques that they can appreciate and use effectively, as well as reinforce their understanding of supply chain theories, principles, and concepts studied previously in foundation courses.
BANA 645. Multimedia Content Analysis (3)
Prerequisite: Graduate standing. This course discusses the business analytics methods and tools that help to systematically extract not only information but also insights from the international media data. In the first part of the course, students get mastery-level experience working with three main pillars of business analytics including, descriptive analytics, predictive analytics, and prescriptive analytics. In the second part, this class explores what metrics are important for decision making in the media industry. This class will equip students to make critical decisions regarding trade-offs in terms of what is most important to decision makers. Media analytics is the art and science of extracting insights from semi-structured and unstructured media data to enable informed and insightful decision making. The science part involves systematically identifying, extracting, and analyzing media data using sophisticated tools and techniques. The art part interprets and aligns the insights gained with business goals and objectives.
BANA 650. Healthcare Analytics (3)
Prerequisite: BANA 607. This course prepares students to understand the process of analyzing electronic health records (EHRs) to improve patient care and to achieve greater efficiencies in healthcare systems. The course enables students to understand the characteristics of the clinical data to derive data-driven solutions. The course covers handling of real-world EHRs, cleansing data, imputing data, and using existing methods to solve major clinical problems, such as readmission, mortality prediction, etc. The key focus is on the interpretation and presentation of the results to practitioners or physicians. The course covers both clinical and administrative aspects of healthcare.
BANA 690A-Z. Selected Topics for Business Analytics (3-3)
Prerequisite: BANA 607. This course focuses on advanced topics in business analytics and relevant applications. It will combine academic rigor and real-world relevance and insight. Students will demonstrate mastery of such topics by putting business analytics knowledge, tools and techniques into practice. Topics will be specified in the Schedule of Classes. This course may be repeated once for credit, provided the topic is different.
BANA 698. Graduate Project (3)
Prerequisite: Completion of all required courses and program director’s consent. This is a culminating graduate project in which small teams of graduate business analytics students demonstrate mastery of the program learning goals through a project in business analytics. Under faculty supervision, graduate business analytics students collaborate with a business partner, including a potential client, faculty advisor, program director, and/or subject matter expert, to determine the scope of work to address a business problem related to a business analytics. Students systematically analyze the business problem by collecting and analyzing business data, using the tools and techniques learned in the graduate business analytics program. They apply research to make evidence-based and well-informed decisions and provide business recommendations to effectively address the problem. They will develop and submit a written report with business recommendations. An oral presentation is also required.