Data Science

Python is rapidly becoming the most important programming language in the world. It is used for everything from Artificial Intelligence (AI) applications and web technologies to financial modelling. Python has grown to be the prescribed language for AI and machine learning applications. Assuming no programming experience, this module prepares you for the exciting world of AI and programming by giving you the fundamental programming knowledge you need in the context of the Python programming language. Following successful completion of the module you are ready for advanced programming concepts.

Dive into the exhilarating world of machine learning, where cutting-edge artificial intelligence meets real-world applications. With our comprehensive program, you'll discover how computers can autonomously learn and evolve, harnessing data and active exploration to perfect their algorithms. By mastering these revolutionary techniques, you'll gain the skills to make insightful, effective decisions tailored to a wide array of practical challenges. Our in-depth exploration of key algorithms ensures you not only understand but excel in the intricacies of machine learning, positioning you at the forefront of innovation. Embrace the future - let machine learning elevate your expertise and ambition to new heights.

We will give you the statistical and computing skills you need to succeed in the rest of the course. Even if you have limited mathematical skills or no computer programming knowledge we will guide you to a level where you can apply advanced data science methods effectively. This module includes subjects such as introductory probability where you’ll learn how likely you are to win at a game of cards; introductory statistics such as testing a hypothesis in a clinical trial; and programming skills in a language such as R, including data visualisation. We will then move on to more advanced topics such as linear regression, which allows you to make predictions using multiple variables, and skills that will increase your employability, such as report writing, production of slides using appropriate software, and presentation skills. You will also find out about the important ethical implications of working as a data scientist.

This module offers a comprehensive exploration of foundational Bayesian algorithms within the realm of probabilistic machine learning, equipping you with cutting-edge techniques applicable across diverse domains including natural language processing, image recognition, and fraud detection. You’ll delve into fundamental Bayesian Inference concepts, including prior and posterior distributions, Bayesian estimation, Bayes factor, model selection, and forecasting. You’ll gain knowledge of various posterior sampling algorithms and see how to apply them through real-world instances in linear regression and classification. You’ll also learn about the latest trends in the field including variational Bayes and online learning. Through a combination of lectures and practical computer-based sessions, you’ll gain hands-on experience and theoretical insights and gain a deep understanding of probabilistic machine learning methodologies.

You will build on the knowledge from Foundations of Data Science, gaining advanced skills that you will use in your career as a data scientist. Our emphasis is on both data modelling skills and the ability to apply this knowledge to messy, real-world data. We will teach you technical skills that are in demand from employers, including different types of sophisticated statistical models and how to apply them using computer languages that are used by many organisations. You will apply these methods in the role of a consultant, working with a stakeholder, such as a client, who has given you a realistic data problem. This will involve working on the problem from the initial meeting, through understanding and analysing the data, to presenting the results back to the client.

Deep learning is an approach to machine learning developed over the past several decades that draws heavily on our knowledge of the human brain, statistics and applied mathematics. In recent years, deep learning has grown tremendously in popularity and usefulness, largely due to more powerful computers, larger datasets and techniques to train deeper networks. You will investigate modern deep neural networks starting from the fundamentals of Artificial Neural Networks (ANNs). You will start by learning what is an artificial neuron and architectures like multilayer perceptron, expanding on algorithms for training ANNs such as gradient descent and backpropagation. You will examine applications of deep learning with particular attention to coding aspects during lectures and practical activities. You will learn to use the most popular techniques to evaluate the performance of a model on real data. For this, you will be introduced to different types of deep neural networks, from convolutional networks to recursive neural networks, and autoencoders to address different problems with a particular focus on computer vision topics.

The data science project is an opportunity to bring together everything you have learned on the course. You will carry out an extended piece of research on an important topic, including the opportunity to propose your own project, exploring the subject in depth using the data science methods that you have studied in the earlier modules. We will guide you through planning the project, providing you with initial comments, and then you will prepare and give a presentation describing your initial findings. The feedback we give you will help you to carry out your subsequent analyses in an effective way. Throughout the project you will be guided by a member of academic staff who will supervise your analyses based on their particular expertise. You will describe your work in an extended written report, which will be evidence of your abilities in data science that you can show to potential employers.