Data Science Applications and Techniques

• Credit value: 15 credits at Level 6
• Module convenor and tutor: David Weston
• Prerequisite: Foundations of Data Science II
• Assessment: a data analysis mini-project (30%) and two-hour examination (70%)

This module covers practical concepts and techniques of data analytics and how to apply them. It provides you with the core skills and expertise needed by data scientists. The module will show you how to use popular and powerful data analysis languages Python and R to solve practical problems based on use cases extracted from real domains.

Indicative module syllabus

• Data science in industry
• Time series forecasting
• Neural/Classification approaches to forecasting
• Practical issues which will include topics such as:
• Handling missing data
• Pitfalls in analysis
• Tools Python and R-TidyVerse

By the end of this module, you will be able to:

• understand and apply common methods used to forecast business and economic data
• interpret the outcomes of deploying techniques for quantitative data analysis
• use Python and R-TidyVerse to perform analysis in real-world datasets.