• Create business analytic models to identify relationships relevant to specific business problems and generalize the application for business decision-making.
• Explain important issues and apply analytical methods that are appropriate for the problem at hand.
• Conduct business analytics by collecting, assessing, and applying business performance data.
• Solve a business problem by using analytic methods to generate relevant insights.
• Communicate the findings of the analysis and evaluate limitations and alternative methods.

Business Analytics is an interdisciplinary field that employs data, statistical algorithms, and predictive models to understand and forecast business outcomes. This course delves into quantitative analytical models to derive actionable insights that guide decision-making processes. Students will develop the necessary competencies to address common challenges faced by general managers and business analysts, exploring the implications of their solutions in a practical business context.

The curriculum is designed to enhance skills relevant to effective business decision-making. It introduces students to essential methods and techniques used in business analytics, including data manipulation and visualization, descriptive statistics, hypothesis testing, regression analysis, time series analysis, and advanced topics such as supervised data mining methods (KNN, Naive Bayes, Decision Trees).

The course is structured around lectures and exercises that progressively build on one another, from foundational concepts to more complex analytical techniques. Key topics include:

1. Introduction to Business Analytics - Overview of the field and its applications in modern business environments.
2. Data Management and Visualization - Techniques for effective data handling and introductory visualization methods.
3. Statistical Foundations - Probability theories, statistical inference, and hypothesis testing essential for analytics.
4. Regression Analysis - Exploration of regression techniques for modeling and predictions.
5. Time Series Analysis and Forecasting - Methods to analyze and predict temporal data trends.
6. Data Mining Techniques - Introduction to basic and advanced data mining methods, including supervised learning algorithms like k-nearest neighbors, naive bayes, and decision trees.

Furthermore, we explore the implications of data quality and integrity and how they influence the solutions to business problems. 

Practical application is a core component, with students learning to collect, structure, analyze, and visually represent data. The course also utilizes case studies and exercises to explore general management problems, providing a hands-on learning experience that mirrors real-world scenarios. Hands-on exercises and group project presentations are integral parts of the curriculum, providing students the opportunity to apply learned concepts in real-world scenarios. Industry experts are invited to enrich the learning experience, offering insights into practical applications of business analytics in various sectors.

For specific session timings and locations, students are advised to consult the CBS Calendar.

A full list of relevant literature will be provided in class. The following works are indicative:

• Jaggia, S., Kelly, A., Lertwachara, K., & Chen, L. (2023). Business Analytics ISE (2nd ed.). ISBN: 9781265087685. Published January 21, 2022.
• Boehmke, B., & Greenwell, B. M. (2019). Hands-On Machine Learning with R (1st ed.). Chapman and Hall/CRC. https:/​/​doi.org/​10.1201/​9780367816377
• Pimpler, E. (2017). Data visualization and exploration with R: A practical guide to using R, RStudio, and Tidyverse for data visualization, exploration, and data science applications. CreateSpace Independent Publishing Platform.