Course overview
The aim of this course is to provide an overview of the tools and techniques of data analytics, and how these can be used by an organisation and to introduce and use some of the common tools for analysis. Introduction to data-driven decision making, associated challenges and ethical considerations. Neural networks: classification problems, error functions, gradient descent, cross-entropy etc. Image analysis: image processing, manipulation, segmentation etc. Convolutional Neural Networks: introduction to CNNs, convolutional layers, stochastic gradient descent, network architectures. Time series analysis. Programming: Python.
Course learning outcomes
- Analyse large datasets using neural networks and Python.
- Analyse images and classify them using convolutional neural networks and Python.
- Understand the data analytics lifecycle and apply it to constrained problems in data analytics.
- Apply data-driven decision-making to real-world problems.