The field of data analytics is a very vibrant and broad field. The amount of data available and computing power have exploded in recent years. There is an increasing demand for business analysts who can select and apply the appropriate methods and interpret the results within the context of the problem. The goal of this course is to learn methods for exploring data and building models with the purpose of supporting data-driven decision making. The content of the course can be grouped as follows: Data Exploration, Probability, Confidence Interval Estimation, Hypothesis Testing and Regression Analysis. The focus will be on exploring realistic business scenarios, analyzing datasets using the appropriate analytical techniques, interpreting the analytic output within the context of the business scenario and translating the statistical results into actionable insights.

In the business press, on TV, and in board rooms, 'machine learning,' 'AI,' 'big data' and 'data analytics' are now hot topics. Vast quantities of data are being generated these days, including new types of data such as web traffic, social network data, and reviews and comments on websites. This data is a valuable resource that, when used correctly, can create a competitive edge for companies. Advances in computing hardware and algorithms have significantly improved the quality of predictions and effectiveness of business applications based on them. Expertise in working with data, and a sound knowledge of data mining/machine learning methods, is a much sought after skill. The course provides an introduction to the key tools and techniques of data mining/machine learning, including classification, prediction, cluster analysis, association rules, and text mining. The methods covered are Linear Regression, Logistic Regression, K-nearest neighbors, Naive Bayes, Classification and Regression Trees, Ensemble methods, Neural Networks, K-Means and Hierarchical Clustering, and Association Rules. The focus throughout will be on business applications. Examples from Marketing, Finance, Healthcare, and Operations will be used to illustrate the breadth of applications.

This is an introductory course in corporate financial management. The course outlines the financial concepts and techniques used to evaluate corporate decisions. The topics include the time value of money, valuation of common securities, and discounted cash flow. The objectives are to introduce the language and structure of finance and to develop the ability to analyze financial decisions.

This is a hands-on and application-orientated course. It not only introduces students to a variety of marketing decision models, but also teaches them how to estimate these models using statistical software (SAS and GLIMMIX) and how to apply them for decision recommendations. Students will "get their hands dirty" working on five case projects, each of which requires them to analyze the business problem, select and estimate relevant models, interpret estimation results, and make decision recommendations.

Overview of financial accounting, periodic financial statements and the financial reporting process. Importance of financial statements as information source for creditors and investors and as a means by which managers can communicate information about their firms.

In this course, we lay the foundation for the core capability of leaders: influence. The ultimate outcome of this course is maximizing your capacity to influence people toward a shared goal. The course is also built around the premise that influence isn't a one-size-fits-all approach. The most effective leaders match their approach to the challenge at hand. In other words, the most effective leaders are adaptive; they diagnose the situation and choose the behavior best suited for it. This course gives you a greater awareness of your readiness to influence others and create a high-impact plan for continuing your development.

Data analytics is extensively employed in practical settings to gain comprehensive insights into data, extract actionable information, and facilitate strategic decision-making. This course is designed to introduce essential statistical techniques tailored for data analytics. Topics covered include data summarization and presentation, confidence intervals, hypothesis testing, and regression analysis. Additionally, the course places a strong emphasis on the practical application of these techniques and encourages students to actively engage with data analysis exercises to enhance their managerial decision-making capabilities.

"A picture speaks more than a thousand words." Welcome to BMSO712! This course focuses on communicating business insights and information by creating advanced visualizations and interactive dashboards using Tableau and Power BI (Business Intelligence Tool). A 100% practical-oriented course and learning-by-doing approach will help the students to solve problems from business domains like marketing, operations, human resources, finance, and so forth.

The course is designed to complement other skills you have developed in different courses at the Smith School of Business, with the basic premise that negotiation skills are needed to implement any technical or analytical solution to different problems at the workplace.