Overview

This module is designed to provide students with a foundational understanding of data science. Throughout the module, we will face various data formats and explore a wide range of associated challenges and solutions. Additionally, students learn how to evaluate proposed solutions and effectively visualise data across various applications. The module is a combination of theoretical knowledge with hands-on practice.

Week 1: Introduction Week 2: Regression Week 3: Regression Week 4: Classification Week 5: Classification Week 6: Time Series Analysis Week 7: Clustering Week 8: Recommender System Week 9: Social Network Analysis Week 10: Association Rule Mining

In each session:

• A real-world problem as a case study is raised. Different types of data such as relational datasets, text, image, sound, graph-structured data, and spatial data are examined.

• Common types of features such as Nominal , Categorical , Numerical , etc. is presented.

• Visualisation tools are presented.

Assessment Strategy

-threshold -Equivalent to 50%.Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

-good -Equivalent to the range 60%-69%.Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

-excellent -Equivalent to the range 70%+.Assemble critically evaluated, relevant areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

Learning Outcomes

• Employ data science techniques with a data-set.

• Evaluate the effacacy of experiments conducted.

• Examine appropriate methods to interpret and visualize data.

• Report results of experiments analysing data.