Online Data Science (Statistics) MSc

Build a firm foundation in programming in Python for data science. Whether you are new to Python or an experienced Python user, you will become a confident programmer. able to independently translate a broad range of data science related problems into functioning computer programs and communicate the results. 

This comprehensive introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing present the basics of Bayesian inference. 

Take an introduction to basic data analysis techniques, which can be used to perform a preliminary investigation of data sets. Exploring this data involves visualising the variables and relationships to help determine outliers, identify trends, suggest suitable statistical models and inform future data gathering. 

Equip yourself with the skills necessary to undertake project work as a data scientist. Project planning, reviewing existing methodologies and the presentation of outputs in different forms all form part of this. You will also understand the ethical considerations of data usage. 

Delve into the complexities of machine learning, a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. You will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. 

Understand the theory of linear models and be able to fit multiple linear regression models to data and interpret the results.  The content will develop your appreciation of the limitations of linear models and the use of link functions to generalise the linear regression model. 

Understand how statistical learning is at the core of the modern world, translating data into knowledge. Online advertising, automated vehicles, stock market trading, transport planning all use statistical models to learn from past data and make decisions about the future. Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. 

Understand methods of analysis that allow you to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains. 

You will explore how statistical methods are utilised to make sense of data with multiple variables, also sometimes called multi-dimensional data, and how to discover patterns and infer valuable information from such data. 

You will plan, carry out and present the results of a short project in data science. The project will be presented in a professional format that could serve as an exemplar of your work for a future employer or client. 

Learn the ability to apply standard methods for random number generation and apply different Monte Carlo methods and develop understanding of the principles and methods of stochastic simulation.  

Take an introduction to Bayesian statistical methods through the consideration of philosophical differences with traditional statistical procedures and the application of Bayesian techniques. You will also be introduced to the ideas of quantitative decision theory and rational decision making.