Cornell Tech Course Planner

This course introduces you to the fundamental concepts of financial accounting and shows how they provide inputs to and use cases for business analytics. Financial accounting information is commonly used by investors, regulators, customers, suppliers, and other stakeholders. Investors include individual shareholders and bondholders, institutional investors (e.g., mutual funds, hedge funds, private equity firms), lenders (e.g., banks), and potential corporate acquirers. Financial accounting information also plays a significant role in managerial performance evaluations and day-to-day business decisions. The focus of this course will be on training you to be a knowledgeable consumer and user of accounting information as a source of both data and questions for business analytics. A firm grasp of the fundamental accounting mechanics and associated data analytics is necessary to become an effective consumer of financial accounting information. Accounting is often described as the “language of business.” Anyone who expects to belong to an organization in which financial information is used to make decisions will benefit from this course. This includes students who plan to start their own companies, or to work in industry, non-profits, governments, and anyone who plans to invest or take an active role in their personal finance and retirement planning.

This course provides an introduction to how AI, analytics, and data science tools can improve decision-making in business and organizations. Topics include machine learning, decision-making under uncertainty, generative AI, and AI agents. The course is application-oriented and emphasizes intuition over technical detail, helping students understand not only how different methods work, but also why they work and when they are useful. Through business examples and practical discussions, students will learn how to evaluate AI-driven opportunities, interpret model outputs, and think critically about the role of AI in organizational decision-making. To support learning, students will be provided with Python code and code snippets throughout the course, though no prior programming experience is required.

All functional areas of business-accounting, finance, marketing, and production-employ microeconomic techniques and principles. The goal of this course is threefold. First, it teaches the economic techniques and principles utilized in the functional areas. Second, it introduces the notion of a market, and teaches the role of market forces in determining the opportunities facing individuals and firms. Third, it teaches students how to think like an economist. The course is divided into three parts: Part I focuses on the basic ideas concerning how markets operate, including topics such as consumer behavior, costs of production, and the operation of what are called perfectly competitive markets. Part II considers market power, i.e., what happens when rather than being a price taker each firm faces its own downward sloping demand curve. Part III considers additional topics including the decision to produce or purchase an input and the role of contracts in business decision making.

This course is designed for an introductory level audience that does not have previous business experience and that is interested in career paths in analytics. It introduces you to the fundamental concepts of marketing that are relevant to marketers and analytics professionals.

This course applies cutting edge behavioral science findings to develop your teamwork and collaboration capabilities for working effectively in an organization. The course is designed to provide you with concepts and competencies to help you throughout your careers. The concepts will include both time-tested ideas and very recent findings, putting you at the cutting edge of thinking in management and organizational behavior. But learning the lessons intellectually is the easy part. You will also have the chance to practice and experiment with these ideas. Through class exercises, cases, and assignments, you will have the opportunity to turn the concepts into competencies.

This course is designed for an introductory level audience that does not have previous business experience and that is interested in career paths in analytics. It is meant to give you a strong base in finance that can be used in your professional career as well as give you the background necessary for Fintech and usage of analytics in Finance. The topics we cover include the time value of money, the methods and principles of capital budgeting, interest rates and bond valuation, stock valuation, how to characterize risk, and how to calculate the cost of capital.

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This 1.5 credit course is based on the MBA Core Course NCC 5080 Managing Operations. It has been adapted to an introductory level audience that does not have previous business experience and that is interested in career paths in analytics. In this course, we shall study the methods involved in the DESIGN, CREATION, and DELIVERY of products and services in all types of industries, manufacturing, supply chains, retailing, financial services, and technology.

We live in an age of data abundance. Vast amounts of data are being collected and analyzed to inform decision-making. Data-driven organizations depend on more than dashboards and algorithms. They need reliable data foundations: well-designed databases, clean data pipelines, reproducible transformations, and analysts who can move comfortably between business questions, data architecture, SQL, Python, and modern analytics tools. This course introduces students to the core concepts and practical skills needed to work with structured data in a business analytics environment. Students will learn the basics of data architecture, relational databases, data modeling, SQL querying, Python-based data treatment, and workflow tools used in modern analytics teams. The course is designed for MS in Business Analytics students who may not all have technical backgrounds. The emphasis is practical and applied. Students will learn how data is structured, how it moves through an organization, how it is cleaned and transformed, and how it can be prepared for analysis, visualization, and decision-making. By the end of the course, students should be able to understand common data architectures, write intermediate SQL queries, perform basic-to-intermediate data transformations in Python, and explain how modern data tools fit into an analytics workflow.

BANA 5165 exposes students to industry applications of business analytics, connecting academic learning with real-world practice through guest speakers, corporate partners, and experiential engagements. Students will learn how analytics, data science, and AI are used to solve business problems, inform strategic decisions, improve operations, and create organizational value. Students are expected to complete assigned readings for each session, participate actively in discussions and activities, and submit written reflections post-session.