5 courses with the subject BSAN, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
BSAN 300Fundamentals of Business Analytics
This course covers key concepts related to predictive and prescriptive analytics by combining information technologies and statistical techniques to extract meaning from organizational data. The course includes hands-on work with data and software. Topics covered include data manipulation, decisions under uncertainty, and decision analytics tools (linear and nonlinear optimization). Students apply predictive and prescriptive analytics techniques in order to understand the business environment and guide business-related decisions. Fall even years. Prerequisite: BUSN 260 Business Analysis Tools; must be a junior or senior or have permission from the instructor. Credits 3 403 LMU Undergraduate Catalog
Statistics for Business Analytics covers fundamental statistical concepts and terms relevant to business analytics, providing a comprehensive overview of the role of statistics in business decision-making. Students will learn to describe and explain statistical methods, interpret data using descriptive and inferential techniques, and apply these techniques to real-world business scenarios. Through the course, students will analyze business data to identify trends, patterns, and relationships, using time series analysis, correlation, and other statistical methods. The course introduces the development and interpretation of predictive models using regression analysis. Fall even years. Credits 3
Data Visualization & Reporting introduces best practices in data visualization for more effective reporting of analytical results in a business context. Students learn analytical methods and technologies used to create dashboards and scorecards for more effective communication of patterns and relationships in data. In this course, effective design of data visualizations, choice of chart type, and the effective use of color and other design characteristics are covered. The course covers both the principles of data visualization, methods and tools used to visualize insights from data, and strategies for the effective communication and reporting of data-based insights. Spring odd years. Credits 3
Predictive Modeling & Prescriptive Analytics provides students with skills applying more advanced predictive and prescriptive analytic techniques utilized in business analytics. In the predictive analytics domain, the focus is on the use of statistical and machine learning techniques to predict or forecast future outcomes. Topics include multiple regression analysis and more advanced hypothesis testing techniques. In the prescriptive analytics domain, the focus is on the use of data-driven models to prescribe the best action plan given a set of conditions or a problem situation. Topics will include spreadsheet modeling, optimization models, simulation, and an introduction to additional machine learning algorithms. The emphasis is on model formulation and interpretation of results. The course covers a wide range of predictive and prescriptive methods that are widely used across the various functional areas of business. Spring even years. Credits 3
Data Mining provides an overview of the principles and techniques of data mining. Data mining is the science of discovering structures and making predictions in large, complex data sets. The course introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions, pattern discovery and cluster analysis. Topics covered include the data mining process, data preprocessing, data mining techniques, and data mining evaluation. Fall odd years. Credits 3
