BAN

Total Credits 9

Subject

BAN

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

BAN 830 explores the use of descriptive analytics concepts, tools, and techniques throughout a wide range of business scenarios and problems. Initially focusing on the application of traditional descriptive analytics techniques to answer the question, "What happened?," the course provides opportunities for students to perform spreadsheet- and programming-based data acquisition, cleaning, manipulation, and e visualization using data derived from various business contexts to inform business leaders' decisions. Later, students progress to applying advanced statistical techniques and concepts including descriptive measures, sampling and estimation, and inference in both spreadsheet and programming environments. The course concludes with a bridge to predictive analytics as students apply linear and multiple linear regression to business-related data in search of relationships among input and output variables. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interests.

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Given society's ever-expanding ability to collect and store vast amounts of transactional, performance, and financial data, business analysts and leaders need the capability to recognize patterns in and transform raw data into actionable business intelligence. Designed for recent graduates with little to no professional experience, BAN 831 expands upon the data visualization concepts covered in BAN 830 by exploring a variety of advanced data visualization techniques focused on "big data" sets derived from marketing, finance, accounting, supply chain management, and other business-related scenarios. Using the latest data visualization . software applications, business students will focus on the development of dashboards and scorecards useful for translating structured and unstructured business performance data into decision-ready knowledge. The course will prepare business analysts by exploring techniques for visualizing data from sales transactions, social media, marketing surveys, financial records, and other sources in support of fact-based decision making. An emphasis will be placed on the nuances specific to decision making in various business areas. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interest.

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Designed specifically for recent graduates with 0-5 years of practical experience, BAN 832 gives business students the foundational programming skills they need to leverage the power of leading edge general purpose programming languages to acquire, clean, manipulate, query, visualize, and analyze large data sets typical of a variety of business environments. With a focus on developing solutions to business data problems, students will become conversant with a variety of y software applications in the context of financial, marketing, supply chain management, and other data-rich business scenarios. Coursework includes individual assignments intended to develop dexterity with foundational programming skills, followed by case-based problems that challenge students' creativity and programming mastery in search of solutions to complex business problems. This course aims to put recent

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

BAN 840 explores the use of predictive analytics tools and techniques throughout a wide range of business scenarios and problems. Initially focusing on the application of traditional predictive analytics techniques to answer the question, "What will happen in the future?", the course provides opportunities for students to apply regression and forecasting techniques to data from various business contexts to inform business leaders¿ decision. Later, students explore various software applications and techniques for acquiring, preparing, and analyzing "big data", recognizing and taking advantage of the exponential growth in the amount of structured and unstructured data generated by and available to businesses. The course next examines cutting-edge techniques gaining increased attention among analytics experts, including data mining, text analytics, and social media analytics. Finally, students will be given an overview of the future of predictive analytics, developing an awareness of artificial intelligence and machine learning concepts, such as neural networks, to help them advance their organizations¿ business analytics capabilities. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interests.

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

BAN 830

Intended for recent graduates with little to no professional experience, BAN 841 develops business students¿ understanding of and ability to apply a variety of data mining tools and techniques for use in detecting and exploiting patterns and relationships in large structured and unstructured data sets derived from a variety of business scenarios. Students will explore the use of cluster analysis, classification, association, and cause-and-effect modeling techniques to explore and reduce data, classify new data elements, identify natural associations among variables, create rules for target marketing or buying recommendations, and describe relationships among data that motivate business performance. Specific techniques may include k-nearest neighbor, discriminant analysis, and association rule mining. Students will learn how to bridge descriptive and predictive analytics across a variety of business scenarios. Coursework includes individual assignments intended to develop confidence with basic data mining techniques, followed by case-based problems that challenge students¿ creativity and data mining mastery in search of patterns and data relationships leading to useful business insights. While underlying theory will be discussed, the course will prepare business analysts by focusing specifically on data mining applications in marketing, finance, supply chain management, and other business areas, with an emphasis on the unique aspects of decision making in a business environment. Software packages, concepts, and business applications will vary and evolve to keep pace with technology, theory, and instructor interest. Graduate - The Pennsylvania State University 2026-2027 983

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Electives 2-3 Elective courses can be chosen from a list of approved courses maintained by the graduate program office. The list of elective courses may change over time based on feedback from students and industry.

Subject

BAN

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu

BAN 886 serves as the first of two courses (BAN 886 and BAN 887) in the two-part capstone course sequence in the Master of Business Analytics program. This course applies graduate studies in business analytics to a hands-on use case that closely mimics real-world analytical consulting work. Students will apply skills in data management, stakeholder management, ethical use of data and analytics, business acumen, and professional communication with a nontechnical stakeholder. The student must work with a consulting team, manage the client relationship, frame a business problem, reframe the business question as an analytics question, and acquire and clean data in preparation for follow-on analysis.

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

BAN 887 serves as the second of two courses (BAN 886 and BAN 887) in the two-part capstone course sequence in the Master of Business Analytics program. This course is a continuation of BAN 886, in which student teams propose and begin an analytical consulting project. Students' business analytics to a hands-on use case that closely mimics real-world analytical consulting work. Students will implement skills in stakeholder engagement and communication and predictive modeling techniques such as supervised and unsupervised machine learning techniques, optimization techniques, and forecasting. This semester emphasizes training models, implementing an analytical solution, deploying a model, and communicating results to nontechnical stakeholders.

Subject

BAN

Credits (min)

3

Credits (max)

3

Credit unit

Credits

Type

course

Edition

graduate

Source

bulletins.psu.edu

Prerequisite

BAN 886

Total Credits 30

Subject

BAN

Credits (min)

3

Credits (max)

3

Type

course

Edition

graduate

Source

bulletins.psu.edu