In this module, we dive into the complexities of big data, exploring diverse data types, sources, and collection methods while learning how to transform raw datasets into meaningful, usable formats through processes such as cleansing and normalisation. By mastering data wrangling and evaluation techniques, you will develop the skills to critically analyse data for quality, reliability, and business impact, preparing you to handle real-world challenges in data science.
Learning Outcomes
I will learn:
- Develop critical understanding of diverse data types, formats, and collection methods, and how these influence the quality and usability of datasets
- To enhance skills in data wrangling, including cleansing, normalisation, and validation, to transform raw data into structured and reliable formats
- Apply data exploration techniques to analyse, interpret, and present information effectively, with consideration for accuracy, readability, and longevity
- To gain awareness of database design and modelling concepts, and their role in managing, storing, and optimising data for analysis
- To critically evaluate challenges and risks associated with big data, including limitations, security issues, and opportunities for innovation.
- To strengthen the ability to connect theory with practice, by using relevant tools and programming approaches to prepare, clean, and optimise big data for real-world applications.
Artefacts
These are the projects carried out to meet those learning outcomes, which are described in the link below.
Meeting Notes
Notes from various meetings, as well as feedback from team members and tutors.
Professional Skills Matrix and Action Plan
What skills have I gained or enhanced as a result of this module and how can I use it? What else do I need to learn?