Cornell Tech Course Planner

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.

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.

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.