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Contemporary data acquisition and analysis present many challenges, therefore they require the use of specialized data preprocessing, aggregation and manipulation techniques. This module introduces a range of tools and techniques necessary for working with data in a variety of formats with a view to developing data-driven applications. The module focuses primarily on developing applications using the Python scripting language and associated libraries and will also introduce a range of associated processing technologies and techniques, such as natural language processing and machine learning.

The module covers the following topics:

• Data types and formats: numerical and time series, textual, unstructured.
• Data sources and interfaces: open data, APIs, social media data.
• Techniques for dealing with large text data sets, including natural language processing and machine learning.
• Developing and evaluating Data-Driven Applications in Python.

The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development, and testing and the deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.

On completion of this module, students will be able to:

LO1: Evaluate the tools and techniques of data interfacing, aggregation, and processing
LO2: Select and apply a range of specialised data types, tools, and techniques for data interfacing, aggregation, and processing
LO3: Experiment with specialised techniques for dealing with complex text data sets, such as Natural Language Processing and machine learning.
LO4: Design, develop and critically evaluate data-driven applications in Python.