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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.

The purpose of this course is to give students a broad understanding of financial statement analysis and valuation issues. The emphasis is on helping students understand the basic equity research process for use in investment and business decision applications. The concepts will be illustrated through a combination of numerical examples, mini-cases, and lectures. Potential topics include information sources, strategic ratio analysis, accounting quality, non-GAAP metrics, cash flow analysis, forecasting, and valuation. The course deliverables include a project/assignment(s) and a final exam.

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.

The Capstone Project course is a semester-long course that represents the culmination of student learning throughout the MSBA degree program. The course involves a data intensive team project in the respective concentration chosen by each student, i.e., finance analytics, marketing analytics, operations and supply chain analytics, or business analytics. Students are expected to work with the data sets throughout the semester to achieve performance according to various project milestones. In this work, they are expected to focus on designing their project goals in order to obtain business-relevant descriptive, predictive, and prescriptive insights, and applying the tools and techniques learned in various required and elective courses to achieve these goals. The course will culminate in final presentations and report out in a residential week at Cornell University's Cornell Tech campus.

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.

This course addresses the paradigm of data-driven and digital operations for maufacturing, services, retail, and e-commerce businesses. Students in the course will explore methods required for the digitalization of businesses, such as data-driven automation, experiment design, predictive and prescriptive analytics, and impact on financial performance, using real-life examples, datasets, and applications.

This course will teach how to use natural language as an input to machine learning algorithms with the goal of modeling topics across businesses and the public equity landscape. Students will gain a comprehensive understanding of ML by seeing its applicability in investing. They will learn foundational aspects of natural language processing, how it fits into the field of analytics, and ways that NLP is used to manage an investment portfolio. Professor Meredith will teach practical techniques students can use for working with text, but will also teach them skills that go beyond just text processing, such as data manipulation using Pandas, using SQL to ingest data into a Python framework, and working with common ML algorithms and how to tune their hyperparameters. Students will walk away with analytical techniques for evaluating models and the core skill of exercising human judgment in evaluating model performance. Ultimately, students will apply all of these concepts by setting up a successful machine learning project on their own.

This is a 1.5 credit course foundational for the MSBA Program. The purpose of this course is to expose students to the programming environment in Python and provide them knowledge and familiarity with different programming concepts such as objects and classes, functions, recursion, computational efficiency, types of variables, data frames, lists, and working with files.

The broad course goal for Management Writing is simple: to help students improve their writing and become an effective writer within the workplace. Whether we refer to this endeavor as workplace writing, management writing, business writing, or anything else, the main focus remains on practical writing that influences decision making.

Students will consider and implement techniques for effective speaking by analyzing and defining the purpose of a message and evaluating the intended audience and its needs. Students will practice methods and techniques that generate effective content in presentations while embracing personal strengths and challenges in non-verbal messaging to inspire and motivate listeners.

This course will teach students how to improve business performance through better accounting. Students will practice the tools to develop key performance indicators (KPIs) and reporting systems to improve the performance of managers and units all across 21st century organizations that are increasingly governed by algorithms.

We will learn the terms of Data Science / AI. We will learn the process of how AI works. We will learn the most basic ML / AI tools ranging from log. regression to deep learning. Data and AI can create a competitive advantage, but only if utilized correctly. Therefore, we will learn a good deal about data-driven business models. AI is changing our society. It's happening right now, and it's shifting global power. We will discuss the risks and issues.

This course develops students' ability to apply artificial intelligence and data science methods to problems across financial services. Students will build proficiency in the core technical toolkit, including alternative and unstructured data, natural language processing, machine learning, and large language model applications, while developing the financial and institutional literacy needed to deploy these tools effectively in practice. The course addresses AI applications across investment management, risk, credit, insurance, and financial operations, with attention to the structural challenges that make financial services a uniquely demanding environment for data science: weak and non-stationary signals, fiduciary and regulatory constraints, model governance, and the human judgment required to work alongside AI systems responsibly. By the end of the course, students will be prepared to design, deploy, evaluate, and govern AI-enabled workflows across a range of financial services contexts.

How can we best guess whether a new product or service will be a hit with customers? Now more than ever, business managers need data-driven insights to predict exactly how consumers will make their buying decisions, and human behavior is at the heart of this puzzle. In this course, you will assess why many common approaches to gauge purchase decisions are not effective. Using many popular products as examples, you will examine how psychology plays a role in how customers behave, applying concepts such as heuristics and biases. You will be introduced to the three key drivers of consumer behavior – attention, emotion, and coherence – and determine how they impact customer behavior. Finally, you will gain applicable consumer behavior insights to help shape your customer-centric marketing strategies. By understanding why customers act the way they do, you and your teams can inform practices to more accurately and successfully bring your new product or service to market.

The term Customer Centric is synonymous with proactive business strategy worldwide. Primarily due to advances in technology, we are experiencing a fundamental shift in business, i.e., the shift from focusing on the product towards focusing on the individual customer as the most critical unit of analysis, in both the Business-to-Business (B2B) and Business-to-Consumer (B2C) sectors. This course aims students to understand the analytical tools for different aspects of customer centricity: as part of a knowledge management system (understanding the customer), as part of the development of strategic competence as a learning organization (building a customer-centric culture), and as a foundation for corporate strategy development and execution (serving the customer).

Students will gain insight into how consumers make decisions through choice-based conjoint analysis; learning to extrapolate, interpret and then optimize data. A further look into digital advertising analytics will require students to explore marketing mix models and how to determine optimal pricing from conceptual and mathematical standpoints.

A supply chain is a network of organizations that are involved in the different processes and activities that produce value in the form of products and services. Supply chain management is an essential tool for firms to achieve profit maximization as well as social responsibility. Innovations arise from three main sources: application of information technology, reconfiguration of the supply chain network, and modification of incentives. In this course, students will explore analytical methods and innovative designs of supply chain management, and be acquainted with some practical applications. Specifically, we will address the following issues: demand forecast and seasonality, supply chain inventory analysis, cross-docking and automation, supply chain coordination, dynamic pricing and revenue management, sustainability and responsible supply chains.

Most organizations are awash in data yet struggle to transform it into meaningful information. In this course, students will master the ability to analyze data efficiently; convey information effectively; and build valuable, robust, and reusable models to support decision making in a professional environment. Students will work with realistic datasets in Excel to build dashboards and models that enable them to evaluate decision alternatives. Students will come away from the program not only with advanced Excel knowledge but with the tools to effectively communicate relevant insights to stakeholders.

Database management is of vital importance to any organization in the modern world given the vast amounts of data involved in business operations. To address that issue, SQL is generally employed as the high level programming language to communicate with, and manipulate, databases. The main objective of this course is to build your ability to write SQL code, ultimately enabling you to efficiently approach and solve complex business problems. By the end of this course you will have a firm grasp of SQL concepts as they relate to data analytics.

The purpose of this course is to introduce you to fundamental concepts in Data Science, Machine Learning (ML), and Artificial Intelligence (AI) to prepare you for your career path as an entrepreneur or various business roles (e.g., product manager, marketing, operations, consulting) in for-profit and nonprofit organizations. This course differs from classes offered from the engineering side in that we use a business-centric approach: we begin with the business problem to be solved, and only then turn to the technical methodologies that solve context-specific use cases. Throughout the course, you will learn a decision-making framework for telling where AI genuinely creates value from where it merely looks impressive — and how to prove the difference. This will be studied with a mixture of business case studies and real-world data sets.