Discussion of different industries and case studies presented throughout the semester. Introduction to Mergers and Acquisitions and the institutions involved in those financial transactions. The course will highlight management consultants’ roles in advising and solving problems for companies. Leadership and business ethics and corporate social responsibility will also be presented and discussed.

Discussion of different industries and case studies presented throughout the semester. Introduction to Mergers and Acquisitions and the institutions involved in those financial transactions. The course will highlight management consultants’ roles in advising and solving problems for companies. Leadership and business ethics and corporate social responsibility will also be presented and discussed.

Analysis of data with appropriate software. Develop data exploration and visualization skills. Apply concepts of random sampling, probability, probability distributions, and expected value. Employ statistical inference in business decision making. Use linear regression for describing and predicting empirical relationships.

Analysis of data with appropriate software. Develop data exploration and visualization skills. Apply concepts of random sampling, probability, probability distributions, and expected value. Employ statistical inference in business decision making. Use linear regression for describing and predicting empirical relationships.

Analysis of data with appropriate software. Develop data exploration and visualization skills. Apply concepts of random sampling, probability, probability distributions, and expected value. Employ statistical inference in business decision making. Use linear regression for describing and predicting empirical relationships.

Introduces students to the language of databases, SQL. Covers the querying of data from databases and manipulating the results of those queries to perform quick and easy analytics. Students will learn how to query multiple tables in a single SQL query. A variety of operations that can be performed on the data retrieved from the tables are also covered. The course will conclude with an exercise to evaluate learning and synthesizing concepts covered in the course.

Introduces students to data analytics with the Python programming language. Covers programming fundamentals and focuses on built-in data structures in Python for storing and manipulating data and statistical analysis and data visualization. Concludes with a data exercise to put knowledge and skills across topics into practice.

Introduces students to data analytics with the R programming language. Covers programming fundamentals and focuses on storing and manipulating data. Concludes with a data exercise to put knowledge and skills across topics into practice.

Introduces students to the thinking behind sustainable business. Students will learn to recognize a sustainable business, understand the benefits this brings to the firm’s stakeholders, and appreciate the challenges sustainable businesses face.

An operational and strategic examination of financial institutions, intermediaries, and innovative startups in payments, digital and mobile banking, alternative lending ("alt-finance"), wealth management, and insurance ("insur-tech"). Also examines the practices, business and revenue models, regulatory issues, and competitive opportunities as well as the challenges and threats FinTech firms must address.

Identify, classify, and operate on key business-related mathematical functions. Use multivariable differential calculus to analyze business margins and solve business optimization problems. Understand multiple regression, ordinary least squares estimation, inference based on OLS. Apply multiple regression for descriptive, predictive, and causal analyses related to business decision-making.

Identify, classify, and operate on key business-related mathematical functions. Use multivariable differential calculus to analyze business margins and solve business optimization problems. Understand multiple regression, ordinary least squares estimation, inference based on OLS. Apply multiple regression for descriptive, predictive, and causal analyses related to business decision-making.