Overview

The Introduction to Data Science module is a dynamic, nine-week course that introduces students to data science essentials. This course covers everything from understanding different data types and collection methods to exploring the applications of AI and statistical analysis. Students will engage in practical projects and case studies, applying theoretical knowledge to real-world scenarios. This approach makes the learning process exciting and prepares students for a career in the ever-evolving data science landscape. By the end of the module, participants will have a solid foundation in data analysis, visualisation, and security, setting them up for success in the digital world.

Key topics: Introduction to Data Data Sources and Collection Data Representation and Visualization AI Applications and Pattern Recognition Statistical Analysis of Data Persistence of Data and Databases Data Integrity and Security Case Studies and Real-World Applications Project and Review

Assessment Strategy

Threshold (40-49% / D- to D+) Has shown knowledge of key areas and principles but there is a weakness in understanding the subject area. Can formulate some appropriate solutions to solve tasks and questions. Outputs can be understood but lack structure and/or coherence.

Satisfactory (50–59% / C- to C+) Has shown knowledge of the key areas and principles and the main elements of the subject area are understood. Can formulate an appropriate solution to solve tasks and questions. Outputs can be understood but there are some weaknesses in the structure and/or coherence.

Good (60-69% / B- to B+) Has shown a strong knowledge and understands most of the subject area. Can formulate appropriate solutions to accurately solve tasks and questions. Solutions demonstrate a good understanding of underlying principles. Outputs are readily understood with an appropriate structure but may lack sophistication.

Excellent (70%-100% / A- to A*) Has shown a comprehensive knowledge and detailed understanding of the subject area. Can formulate appropriate solutions to accurately solve tasks and questions. Solutions demonstrate a good understanding of underlying principles. Presents output in a cohesive, accurate and efficient manner.

Learning Outcomes

• Define data, differentiate between its forms, and recognise examples in everyday contexts.

• Explain the varied uses of data in visualisation, AI applications (such as language models and generative AI), pattern recognition, machine learning, and statistical analysis.

• Identify diverse data sources, discuss their significance and understand strategies for maintaining data integrity and security.