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Analytical Chemistry is a measurement science consisting of a set of powerful ideas and methods that provide qualitative or quantitative information about the chemical composition of a sample. Analytical measurements are required in a wide range of fields beyond the chemical industry such as biochemistry and the pharmaceutical industry, environmental sciences, forensic sciences, and the food industry amongst others. The module will provide an introduction into the fundamentals of chemical analysis, including an understanding of sample preparation, chromatographic separations, and some of the most important analytical techniques today, including IR, NMR, Raman and UV-visible spectroscopies, and mass spectrometry.

Analytical Chemistry is a measurement science consisting of a set of powerful ideas and methods that provide qualitative or quantitative information about the chemical composition of a sample. Analytical measurements are required in a wide range of fields beyond the chemical industry such as biochemistry and the pharmaceutical industry, environmental sciences, forensic sciences, and the food industry amongst others. The module will provide an introduction into the fundamentals of chemical analysis, including an understanding of sample preparation, chromatographic separations, and some of the most important analytical techniques today, including IR, NMR, Raman and UV-visible spectroscopies, and mass spectrometry.

Analytical Chemistry is a measurement science consisting of a set of powerful ideas and methods that provide qualitative or quantitative information about the chemical composition of a sample. Analytical measurements are required in a wide range of fields beyond the chemical industry such as biochemistry and the pharmaceutical industry, environmental sciences, forensic sciences, and the food industry amongst others. The module will provide an introduction into the fundamentals of chemical analysis, including an understanding of sample preparation, chromatographic separations, and some of the most important analytical techniques today, including IR, NMR, Raman and UV-visible spectroscopies, and mass spectrometry.

The first case study is run by a company over one day and focuses on the modelling of a problem that is of current interest. The second case study runs over four days and requires a complex problem to be solved. The techniques used to tackle the problems may be drawn from many of the modules previously studied. The time constraints are such that good teamwork skills are needed to complete the work to a satisfactory standard by the deadline.

The first case study is run by a company over one day and focuses on the modelling of a problem that is of current interest. The second case study runs over four days and requires a complex problem to be solved. The techniques used to tackle the problems may be drawn from many of the modules previously studied. The time constraints are such that good teamwork skills are needed to complete the work to a satisfactory standard by the deadline.

The first case study is run by a company over one day and focuses on the modelling of a problem that is of current interest. The second case study runs over four days and requires a complex problem to be solved. The techniques used to tackle the problems may be drawn from many of the modules previously studied. The time constraints are such that good teamwork skills are needed to complete the work to a satisfactory standard by the deadline.

This module introduces some key concepts about the use of some basic statistical and analytical techniques within the marketing context. Students will learn through a combination of lectures, group work, practical (computer-lab) sessions (where needed), and self-study. After studying this module, students will be able to apply these techniques to analyse data in practice.

This course provides essential knowledge and skills required to support students’ future learning in future data analytic modules.

Without prerequisite knowledge in data analytics, this module will cover spreadsheet analytics and relational database tools used in many business cases.

The spreadsheet modelling includes an introduction to basic spreadsheet functions, moving onto a range of more complex functions and a variety of data manipulation methods, including producing appropriate graphs. Students will experience the range of what is possible within spreadsheets through learning specific techniques.

The relational database section starts with database modelling, database implementation, query execution and business problem solving using Structured query language (SQL). The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

This course provides essential knowledge and skills required to support students’ future learning in future data analytic modules.

Without prerequisite knowledge in data analytics, this module will cover spreadsheet analytics and relational database tools used in many business cases.

The spreadsheet modelling includes an introduction to basic spreadsheet functions, moving onto a range of more complex functions and a variety of data manipulation methods, including producing appropriate graphs. Students will experience the range of what is possible within spreadsheets through learning specific techniques.

The relational database section starts with database modelling, database implementation, query execution and business problem solving using Structured query language (SQL). The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

Referred to as the ‘the new oil’, the importance of data as a resource to modern society cannot be underestimated. Yet many individuals and organisations alike do not use data effectively, if at all. Effective use of ‘big data’, with its vast sizes and numerous formats that arrive at high velocity and vary in multiple dimensions, is especially challenging.

A picture is worth a thousand words. Visualisation is one of the quickest, yet most effective techniques, for understanding and gaining insights from data. Visualisation is particularly effective at uncovering complex relationships among hundreds, or even thousands, of factors to determine their relative importance. Sophisticated analyses can be performed quickly, even immediately, and results presented in ways that showcase patterns so that the data can be used to aid faster, more effective decision-making.

The purpose of this module is to equip you with the knowledge and skills for analysing and interpreting big data using visualisation techniques. The module will explore some of the theories that explain why visualisation is one of the most effective communication tools, the guiding principles of effective visualisation; and the nature of big data, its unique challenges and the way visualisation analytics must adapt to make sense of big data. You will learn, by examining numerous real-world datasets from a wide range of disciplines, how to create engaging, informative, compelling visualisations that reveal the core story hidden within a dataset, no matter how large and complex.

Part art, part science and quite unlike other data analytics techniques, data visualisation is less reliant on subject specific skills, such as statistical computer programming. Thus, this module will suit anyone who has an interest in making the most effective use of data, enjoys being creative, and is reasonably comfortable with using a computer.