Here is a brief overview of the modules you will study on your course.

• Modelling, Regression And Machine Learning – This course will equip you with the theoretical and algorithmic basis for understanding learning systems and the associated issues with very large datasets/data dimensionalities. You will be introduced to algorithmic approaches to learning from exemplar data and will learn the process of representing training data within appropriate feature spaces for the purposes of classification. You will also focus on basic data structures and algorithms for efficient data storage and manipulation. The major classifier types are taught before introducing the specific instances of classifiers along with appropriate training protocols. You will explore where classifiers have a relationship to statistical theory as well as notions of structural risk with respect to model fitting. You will be equipped with techniques for managing this in practical contexts.
• Visual Data Analysis – This module provides an understanding of the methods, theories and techniques relevant to interactive visual data analysis. You will learn relevant principles and practices in visual data analysis design, implementation, and evaluation. You will gain experience in researching, designing, implementing, and evaluating your own visual analysis solutions, using both off-the-shelf tool-kits and data visualisation programming libraries. You will gain the knowledge to support your future employment or research in the fast-developing areas of data science, particularly visual analytics.
• Applied Data Analytics: Tools, Practical Big Data Handling, Cloud Distribution – This course will provide an in-depth of the tools and systems used for mining massive dataset and, more in general, an introduction to the fascinating emerging field of Data Science. The module is divided in two parts: The first part focuses on the languages Python and R, a statistical learning language used to learn from data. This part provides an overview of the most common data mining and machine learning algorithms and every discussed concept is accompanied by illustrative examples written in Python and R languages. The second part of the module takes a tour through cloud computing and big data systems and teaches the participant how to effectively use them. Specifically, platforms and systems like OpenStack, Hadoop, MapReduce, MongoDB, Spark and NoSQL databases are introduced and every concept is accompanied by a number of illustrative examples.
• Legal, Ethical and Security Aspects of Data Management – This module focuses on legal, ethical and security requirements that underpin the technical processes and practice of data science (the collection, preparation, management, analysis and interpreting of large amounts of data called big data). Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services among others. The volume of data collected, stored and processed brings many concerns especially related to privacy, data protection, liability, ownership and licensing of intellectual property rights and information security. This module will explore how data can be fairly and lawfully processed and protected by legal and technical means. It will give students a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and important information security management policies that impact on the practice of data science. Further it will equip student with the necessary foundations to develop high professional standards when working as data scientists.
• Individual Data Science Project  – The project module aims to develop your knowledge and skills required for planning and executing research projects such as proof of concept projects or empirical studies related to data science. To plan and carry out your projects you will have to:
  • Apply theories, methods and techniques previously learned.
  • Critically analyse and evaluate research results drawing on knowledge from other modules.
  • Develop your communication skills to enable you to communicate your findings competently in written and oral form.