Course pre-requisite(s): Basic knowledge of statistics or data analysis (recommended, not mandatory)

Course Overview

This course introduces the fundamentals of business analytics and its role in modern decision-making. Students will learn how to extract insights from data using statistical and machine learning techniques, explore practical applications in business, and
understand the analytical process from data preparation to model interpretation. Through interactive sessions and hands-on exercises, participants will gain experience applying analytics tools and methods to real datasets using Python.

Learning Outcomes

By the end of this course, students will be able to:
• Understand the main steps of the analytical process and the distinction between descriptive, predictive, and prescriptive analytics.
• Prepare, clean, and explore datasets to extract meaningful insights.
• Apply and interpret predictive models such as decision trees.
• Evaluate model performance and communicate analytical findings effectively.
• Formulate data-driven recommendations for managerial decision-making.

Course Content

1. Introduction to Business Analytics and the Analytical Process
2. Data Preparation and Data Quality
3. Exploratory Data Analysis and Visualization
4. Introduction to Predictive Modeling
5. Model Evaluation and Interpretation
6. Communicating Insights and Data-Driven Decision-Making
7. Applied Case Study in Business or Healthcare
(Topics such as advanced machine learning, deep learning, or big data architectures are not covered in this introductory course.)

Instructional Method

The course combines interactive lectures, hands-on exercises, and case-based discussions. Students will work individually and in small groups to explore datasets, build simple predictive models, and interpret results. A practical, application-oriented approach will be emphasized throughout.

Required Course Materials

• Lecture slides and datasets provided by the instructor
• Access to Python (Google Colab)
• Recommended reading:
o Fundamentals of Machine Learning for Predictive Analytics – John D. Kelleher, Brian Mac Namee, Aoife D'Arcy, 2015. Chapters 1, 2, 3, 4, 8.

Assessment

Assessment will be based on a combination of:
• Class participation and exercises (30%) – completion of hands-on tasks and discussions.
• Mini-project (40%) – application of analytics methods to a dataset, including a short written report.
• Final reflection (30%) – interpretation and communication of results linking analytics to business decisions.
The evaluation aims to foster active learning and ensure that students not only apply analytical methods but also understand their implications in decision-making contexts.