COMP1961
Essentials of Data Science

This module aims to provide students with a comprehensive understanding of data management, analytics, and visualisation within modern data analytics ecosystems.
It covers relational and NoSQL database design, normalisation, and querying, alongside big data concepts, cloud-based solutions, and advanced visualisation techniques.
Students will explore industry-standard tools and frameworks, examine ethical and societal implications, and employ advanced methods in data analytics and intelligent systems for practical applications.

1. Integrate and justify the use of sophisticated techniques for the design and implementation of complex database systems, demonstrating informed judgment and awareness of current research and professional practice.
2. Exercise autonomy, collaboration and systematic reasoning to analyse, assess, and resolve diverse database challenges within defined organisational and business constraints.
3. Critically appraise and apply emerging database technologies to develop innovative solutions aligned with evolving business requirements and strategic objectives.
4. Synthesise and evaluate the theoretical frameworks and design principles that underpin advanced data visualisation practice.
5. Justify the selection of specialised tools and methods to construct and optimise data visualisation pipelines for varied data contexts.
6. Interpret and critically assess data-driven insights derived from advanced analytics outputs, applying evidence-based reasoning to inform decision-making.

Coursework: 50% weighting, 50% pass mark.
Learning Outcomes: 1 - 3
Word Length: 2750
Outline Details: Design, implementation and evaluation of a database system supporting data management, organisation and modelling for business needs.

Project: 50% weighting, 50% pass mark.
Learning Outcomes: 4 - 6
Word Length: 2750
Outline Details: Creation of an interactive dashboard that effectively visualises complex datasets to support critical insight and decision making for a business.

• Relational databases basics- relationships, entities, attributes, keys, ERD
• Database normalisation, SQL
• Big Data – application examples, analytics phases
• NoSQL databases, MogoDB
• Data visualisation- techniques, tools, libraries
• Big data visualisation, challenges, approaches, industry leading tools and Open-source options
• Cloud solutions for big data analytics and storage
• Dashboards for big data
• User experience and domain context influences on the approaches to data analytics and visualisation
• Knowledge Systems- recommenders, Chatbots, Virtual Personal Assistants
• Applications including Netflix recommendation/personalisation, Spark streaming social media posts etc.
• Implementation Issues, ethical considerations and privacy, organisational and Societal Impacts