• 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 leverages data, statistical models, and predictive algorithms to analyze business dynamics and support data-driven decision-making. This course equips students with the analytical competencies necessary to address managerial challenges, interpret complex datasets, and derive actionable insights within a practical business context.

The curriculum is designed to develop proficiency in key business analytics techniques. Students will gain expertise in data manipulation, visualization, statistical inference, hypothesis testing, regression analysis, time series forecasting, and machine learning methods. The course also introduces supervised learning techniques, including k-nearest neighbors (KNN), naïve Bayes, decision trees, and support vector machines, alongside an introduction to deep learning models and eXtreme gradient boosting (XGBoost).

The course follows a structured progression, starting with fundamental concepts and advancing to complex analytical methods. The core topics include:

• Introduction to Business Analytics – Overview of the field and its applications in modern business environments.
• Data Management and Visualization – Techniques for data handling, cleaning, and visualization.
• Statistical Foundations – Probability theory, statistical inference, and hypothesis testing.
• Regression Analysis – Linear and multiple regression models for predictive analytics.
• Time Series Analysis and Forecasting – Methods for analyzing and predicting temporal trends.
• Supervised Learning – Fundamental and advanced machine learning techniques, including KNN, naïve Bayes, support vector machines, decision trees, and random forests.
• Deep Learning – Introduction to gradient boosting models (e.g., XGBoost) and deep learning architectures.
• Data Quality and Integrity – Examination of data reliability and its impact on analytical outcomes.

A strong emphasis is placed on hands-on learning. Students will engage in:

• Data collection, structuring, analysis, and visualization using industry-standard tools.
• Case studies that apply business analytics techniques to real-world managerial problems.
• Hands-on exercises and group project presentations that reinforce theoretical knowledge through practical implementation.
• Industry expert sessions, offering insights into business analytics applications across various sectors.

Students are expected to actively participate in all course components. For session schedules and locations, please refer to the CBS Calendar.

• Models

• Development of research questions
• Data collection
• Analysis
• Discussion, critical reflection, modelling

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, Second Edition. ISBN: 9781265087685.
• Chollet, F., Kalinowski, T., Allaire, J. J. (2022). Deep Learning with R, Second Edition. Manning Publications. ISBN: 9781633439849
• Boehmke, B., & Greenwell, B. M. (2019). Hands-On Machine Learning with R (1st ed.). Chapman and Hall/CRC. https:/​/​bradleyboehmke.github.io/​HOML/​