Machine Learning and Data Science Skills for Data Driven Decision Making

Course description:

This module's interactive learning sessions allow students to acquire the hands-on and on-screen experience they need in exploring the rapidly evolving landscape of machine learning and data science. Students will work collaboratively to draw conclusions and extract useful information from available datasets while gaining the invaluable skills on how to interpret and report their analysis and results for informed decision-making purposes.

This is a practical module that introduces the concepts of machine learning and application of algorithms to several types of available data samples.  In order to achieve this student will be introduced to the Python programming language and key concepts related to the TensorFlow (TM) programming toolkit from Google. At the end of the module students will have learned how to train machine learning algorithms and evaluate their performance on research data. Programming skills will be developed during this module to explore the potential benefits of deep learning algorithms.  

The module also aims to address the current needs of the prospective students to develop the following most in-demand skills in data science: how to use scientific computing methods to handle, cleanse, transform, and validate data with the purpose of gaining insights from a wide range of datasets; how to present available data using charts, graphs, tables and more sophisticated visualisation tools; how to model data and perform statistical analysis and ad hoc queries; how to report on key findings and useful information extracted from analysed datasets and how to summarise and communicate results to mixed audiences. 

Learning outcomes:

You will learn/develop:

• basic commands in Python and learn how to manipulate data using this programming language

• how to use TensorFlowTM tools to optimise neural networks and convolutional neural networks as examples of machine-learning algorithms

• a comprehension of machine-learning algorithms and their use.