There are no pre-requisites for this module to be added

This module will explore and develop data management and processing solutions that work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.First, modern tools and techniques in data management will be examined, emphasizing good practice and professional approaches to storing and handling data. Next, the module will explore ways of cleaning noisy real-world data to make it suitable for data processing. Finally, data processing and collation techniques such as Machine learning or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, the advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:• Data warehousing and storage techniques• Data cleaning techniques• A discussion of cloud approaches• Data processing and collation techniques• An introduction to parallel data pipeline approaches• An introduction to Natural language processing, and • Topic modelling

Upon completion of this module you will be able to

LO1: Compare different data warehousing techniques and technologies related to data management.

LO2: Critically reflect on the advantages of local and cloud solutions for data processing.

LO3: Appraise different methods of data cleaning in the context of large or complex data sets using R language.

LO4: Integrate industry-standard data collation techniques.

LO5: Create a data processing and management pipeline from raw data to a final delivery using R language.