Aims

This course introduces the fundamental principles of data representation, management, and analysis, with a focus on applications in sports science. Students learn how to interpret different kinds of data, understand how digital information is represented and processed, and use Microsoft Excel for practical analysis, visualization, and reporting.

By combining conceptual foundations with hands-on activities, the course helps students develop the skills needed to organize data, extract useful information, and support decision-making in training contexts.

Teacher

• Michael Soprano - Course Leader

The course consists of 12 lectures for a total of 24 hours. Assessment is based on a written exam with applied components. The course is delivered in the first semester.

Topics Covered

Module 1 - Introduction to Data Science

• Data and information in the context of sport
• From raw data to information
• Populations, variables, and levels of measurement
• Types of variables and types of data

Module 2 - Representation and Management of Data

• Data storage in the filesystem
• How data is represented in digital form
• Text: character sets and encoding
• Images: raster and vector graphics, and main file formats
• Sound: sampling, frequency, and bit depth
• Video: sequences of images, key characteristics, and formats
• The Shannon-Weaver model of communication
• Entropy and redundancy of information
• Why data compression is useful
• Lossless compression
• Lossy compression

Module 3 - Data Analysis with Microsoft Excel

• Elements of the Excel interface and workbooks
• Managing cells, rows, columns, and ranges
• Entering, editing, and formatting data
• Applying numeric, date, and text formats
• Working with ranges and data tables
• Introduction to formulas and functions
• Formula syntax: references, operators, and errors
• Function syntax: arguments, result types, and nesting
• Importing structured data from text files
• Creating and managing charts
• Examples: line, scatter, and area charts
• Introduction to PivotTables
• Practical activities on real datasets, including FitBit data and the Boston Marathon 2025

Learning Approach

The course is organized into three modules that progressively introduce students to the role of data in sports science, the representation and management of multimedia data, and the practical use of Excel for data analysis. Through examples and applied activities, students develop both conceptual understanding and operational skills.

Reading Material

• Peter O’Donoghue, Lucy Holmes, Data Analysis in Sport. Routledge Studies in Sports Performance Analysis, First edition, 2014
• Michael Alexander, Dick Kusleika, Excel 365 Bible. Wiley, First edition, 2022