This course introduces students to key business topics relevant to high technology companies. Students will learn how to understand and interpret financial statements and frame financial decisions, including building a business case. The course will explore typical organizational structures and the roles of business functions. Students will be introduced to business models and other concepts in marketing and business strategy.

Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

Will expose student to fundamental coding languages in data science. Python and R will be the primary focus of the course. Popular packages such as pandas and tidyverse will be covered in depth. Additionally, project management skills such as Git and Github will be covered.

Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.

Exposes students to foundational knowledge in the area of analytics, especially as it relates to machine learning. The focus is on methods needed to prepare data for machine learning models, how to evaluate the output of ML models and engineering features.

Course provides students experience w/data science within a framework of data ethics in service of equity-oriented public policy. Primary goals are:collaborate w/community partner on project that advances social justice and policy understanding; practice working with real data and moral & ethical implications of work; and develop experience in data workflows that support ethical data science.

Introduction to core data science concepts and skills, including computing environments, visualization, modeling, and bias analysis. Think like a Data Scientist as you engage through lectures, discussions, labs, and guest talks while applying learning in a guided semester-long project. Concludes with an independent project to reinforce and extend skills.

Students will learn the fundamentals of product management. Topics include identifying unmet needs, understanding markets, implementing product development frameworks and processes, building businesses, and working with multi-functional teams. The application of these concepts to different phases of the product lifecycle will be explored. Students will build technical, professional, and soft skills necessary for success in product management. Prerequisite: EBUS 1800 and enrolled in the Engineering Business Minor or Entrepreneurship Minor - Tech Concentration and 3rd or 4th year standing

Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and network centrality.

Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics include we will cover are an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.