The areas covered in this module include probability theory, likelihood, common distributions, confidence intervals, hypothesis tests, parametric and non-parametric tests.
Upon successful completion of this module, you will be able to:
- demonstrate the ability to critically appraise and evaluate mathematical and statistical techniques for the given empirical/data analysis.
- understand the physical significance of the given mathematical and statistical technique.
- use the optimisation techniques in decision making.
- use the statistically significant conclusions from the sample data.
- Exploratory Data Analysis (EDA)
- Data Pre-processing, Correlation and Probability Overview
- Sampling and Hypothesis Tests
- Significance Tests
- Linear Regression
- Logistic Regression (LR)
- Extreme Gradient Boosting (XGBoost)
- Working with Imbalanced Data
- Unsupervised Learning and Feature Selection
- Machine Learning on the Cloud (AWS as an example)
15 (150 hours)
- Summative coursework (30%)
- Written examination (70%)