The aim of this module is to familiarise students with the whole pipeline of processing, analysing, presenting and making decisions using data. This is a research-led, MSc level course, where students are expected to develop a complete end-to-end data science application. The course blends data analysis, decision making and visualisation with practical python programming. The module assumes a reasonable programming background and is not suitable for students without prior programming experience.
The aim of this module is to equip students with the theoretical tools and practical understanding necessary to create end-to-end data science applications, all the way from the initial concept to final deliverable.
After completing this module, students will be expected to:
Understand the basics of the python data science and decision making
Summary and re-sampling statistics (cross-validation, permutation tests, bootstrapping).
Predictive Modelling and related methods
Data Exploration: Clustering methods, Dimensionality Reduction, Data Transformation
Dataset Shift and Transfer Learning
Deep Learning for Images and Text (Convolution Neural Networks and Recurrent Neural Networks)
Generative Models: Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs)
Dask and Unix
I have shared this module with Dr Ana Matran-Fernandez and Dr Spyros Samothrakis
Lab work is available GitHub
Teaching Materials Available via Moodle