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Aims

The course introduces the fundamental principles of data representation and processing, with a focus on real-world applications in the field of sports science. It provides students with the ability to recognize, structure, and interpret different types of data—textual, visual, audio, and numeric—and to use Microsoft Excel for practical analysis, visualization, and reporting.

By exploring how raw data is encoded, stored, compressed, and transformed into usable information, students acquire both conceptual tools and technical skills to support decision-making and communication in training contexts.

Teacher

• Michael Soprano — Course Leader

The course includes 12 lectures totaling 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 (2h)

• What is data?
• Data in the context of sport
• From raw data to information
• Populations, variables, and levels of measurement
• Specific data types and practical relevance

Module 2 — Data Representation and Encoding (8h)

• How data is stored in the filesystem
• Representing data in different formats
• Introduction to semiotics and symbolic systems
• Text: characters and encoding standards (ASCII, Unicode)
• Images: bitmap vs vector graphics, formats and compression
• Sound: sampling, MIDI messages, audio file formats
• Video: digitization, frame rate, codecs
• The Shannon-Weaver model of communication
• Entropy and redundancy
• Principles of data compression
• Lossless compression methods (e.g. ZIP, RLE)
• Lossy compression methods (e.g. JPEG, MP3)

Module 3 — Data Analysis with Microsoft Excel (14h)

• Overview of the spreadsheet interface
• Navigating and editing cells, rows, and columns
• Entering and formatting data in a workbook
• Case study: fitness center budget
• Working with ranges and data blocks
• Introduction to formulas and functions
• Formula syntax, operators, and common errors
• Function types: mathematical, logical, statistical
• Importing structured data from text files (CSV, TSV)
• Creating and customizing charts (line, area, scatter)
• Introduction to PivotTables
• Building and managing PivotTables for structured analysis

Learning Approach

The course emphasizes clarity, relevance, and hands-on application. Through examples and case studies drawn from the domain of sports, students progressively build the ability to describe data, apply encoding models, and work effectively with spreadsheet tools for analysis and reporting. Active use of Excel is integrated in all practical modules.

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