Foundations of Business Analytics
Crafting Narratives from Numbers
Welcome
In this course, you’ll develop the essential skills to analyze and interpret data for better business decision-making. Whether you’re new to coding or looking to strengthen your analytical thinking, we’ll guide you through the fundamentals of working with data in R — structuring, transforming, visualizing, analyzing, and interpreting information effectively.
Business analytics spans three critical dimensions: understanding what happened in the past, anticipating what will happen next, and uncovering why things happen through causal analysis. Through hands-on case studies, you’ll apply these approaches to real-world business problems, extracting meaningful insights that drive action. The power of data lies in the stories it tells. From ancient oral traditions to modern visualizations, effectively communicated insights create impact. You’ll learn to transform raw data into compelling narratives that enhance clarity and directly influence business decisions.
Your approach will mirror that of a scientist: observing carefully, experimenting methodically, and making evidence-based predictions about firm and consumer behavior. This systematic framework provides a practical toolkit for addressing complex business challenges. With this analytical power comes significant responsibility. We’ll explore ethical dimensions of working with sensitive information, recognizing and mitigating bias, balancing analytical insight with individual rights, and ensuring transparent communication of findings.
For knowledge to create value, it must be communicated clearly and transparently. You’ll develop workflows that ensure accuracy, consistency, and reliability — critical skills for building trust in your analysis.
Data is everywhere, but enduring insights are rare. This course will help you transform numbers into knowledge that informs business and society—ethically, responsibly, and effectively.
Audience and Assumed Knowledge
This course is designed for first-year undergraduate students enrolled in business, commerce, or economics degrees.
We assume students:
- Have no prior programming experience.
- Have basic numeracy skills (high school level mathematics).
- Are curious about how data and technology are changing the way businesses operate.
- Are ready to engage in structured problem-solving and critical thinking.
No specific technical prerequisites are required. Our goal is to meet students where they are and progressively build their skills, confidence, and analytic thinking.
We welcome students from all backgrounds — no prior coding or analytics experience is needed, and we will build every concept step-by-step. Curiosity, patience, and a willingness to engage with new ideas are the most important starting points.
Learning Goals
By the end of this course, students will be able to:
- Define and explain key concepts in business analytics.
- Identify different types of business problems and select appropriate analytic approaches.
- Critically assess data sources and recognize the limitations of data-driven insights.
- Communicate analytic findings through clear, structured narratives.
Pedagogical Approach
Our teaching philosophy blends three core elements:
- Business Challenge First
- Each chapter begins with a real-world business problem or question.
- Readers are invited to think creatively and critically about how they would approach the challenge before technical methods are introduced.
- Skills of a Business Scientist
- We teach how to combine technical data skills, business judgment, and communication.
- Emphasis is placed on asking good questions, interpreting results thoughtfully, and communicating clearly.
- Data to Narrative
- Students learn how to move from raw data, through analysis, to actionable insights.
- Storytelling is treated as a fundamental analytic skill: interpreting patterns, understanding context, and communicating findings persuasively to decision-makers.
This text is designed to serve both as a foundation for classroom discussion and as a self-contained guide for independent study. Analytics is about exploration and iteration — it is normal to encounter challenges and learn through the process. We encourage a growth mindset: thoughtful questions, careful reasoning, and creative thinking are celebrated throughout the journey into business analytics.
About the Instructors
Lachlan Deer is a Lecturer in the Management & Marketing Group at the University of Melbourne. His research interests lie in quantitative marketing, digital markets and public policy. Lachlan has spent the last decade introducing computing and data science skills into business, economics and social science curriculums, adopting a hands-on and learner focussed practical training, enabling learners to become more efficient and productive in their work by bridging the gap between the increasing reliance on computational skills demanded by employers and the limited training many learners receive in these areas in traditional courses in these areas. Prior to joining University of Melbourne, Lachlan has worked at Tilburg University and the University of Chicago Booth School of Business. Lachlan earned his PhD in Economics from the University of Zurich.
Patrick Ferguson is a Senior Lecturer in the Accounting Group at the University of Melbourne and a Research Affiliate at the Laboratory for Innovation Science at Harvard University. In his research, Patrick uses observational data and field experiments to study how organizations measure, reward, and report on worker performance. He is also interested in contest design and sports economics. He received a PhD in Business Administration from Harvard University.
Junhao Liu is a Senior Lecturer in the Accounting Group at the University of Melbourne. Junhao’s research interests broadly pertain to archival research in financial accounting, and he is particularly interested in accounting intangibles, financial reporting and disclosure, financial analysts, and valuation. Junhao obtained my Ph.D. degree in accounting from Rotman School of Management at University of Toronto.