Further Machine Learning and Analytics

• Credit value: 15 credits at Level 6
• Convenor: Dr David Weston
• Assessment: a data analysis mini-project (30%) and two-hour examination (70%)

In this module we cover practical concepts and techniques of data analytics and how to apply them. We 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 syllabus

• Model performance, interpretability and fairness
• Time series: trend and seasonality
• Time series: autocorrelation and simple predictive models
• ARIMA and beyond
• Multivariate regression
• Feature engineering - for regression, classification and time series
• Missing data
• Class imbalance and anomaly detection

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.