This module covers both database and data science concepts:



Key aspects of data science

Principles of data science

Key case studies to evaluate data science

The fit between databases and data science

Advanced database architectures, N-Tier, Grid Computing, and Distributed Databases

Issues with cloud storage and the impact on performance and design

Security issues with data, in transit and at rest

Data Models, Relational and Object-Relational technologies,

Query languages including advanced SQL and Object SQL;

Advanced Design and design issues; database development and performance, and sharding etc

Current trends in Database development, including knowledge management, web and mobile databases; database issues for complex data including forensic and biometric data.

Concepts and constructs of NOSQL / non relational data

Practical Assessment – A practical assessment which explores the design, creation and testing of an advanced database in relation to a given commercial case study (Learning Outcomes 2 and 3).

Written Report- A written report that documents architecture and database approaches and their fit to data science. The report also covers the students viewpoint on the future direction of database technology (Learning Outcomes 1 and 4)

1. EXPLAIN AND CRITICALLY EVALUATE CONTEMPORARY DATABASE ARCHITECTURES AND DATABASE MANAGEMENT ISSUES IN CONJUNCTION WITH DATA SCIENCE.

Analysis, Knowledge and Understanding

2. CREATE, IMPLEMENT AND CRITICALLY TEST AND EVALUATE AN ADVANCED DATABASE DESIGN

Application, Problem Solving

3. ANALYSE AND CRITICALLY EVALUATE DATABASE MODELS AND QUERY LANGUAGES.

Analysis, Enquiry

4. CRITICALLY DISCUSS THE PROJECTED FUTURE DIRECTIONS OF DATABASE TECHNOLOGY IN TERMS OF DESIGN, HARDWARE AND SOFTWARE IMPLEMENTATIONS.

Learning, Reflection

All teaching sessions will blend theory and practical learning. Students will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples within the same sessions. In addition, students will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.

Database software

Blackboard

Access to a standard PC

All texts and electronic resources will be updated and refreshed on an annual basis and available for students via the online Study Links resource platform. All reference materials will be collated and curated and aligned to Equality, Diversity & Inclusion indicators.

Palicio, A, B. (2021), Distributed Data Systems with Azure Databricks: Create, deploy, and manage enterprise data pipelines,¿Kindle Edition

Vittilo, R. (2022), Understanding Distributed Systems, Second Edition: What every developer should know about large distributed applications, Roberto Vitillo¿

Khononov, K. (2021), Learning Domain-Driven Design: Aligning Software Architecture and Business Strategy, O’Reilly Media

IP Specialist (2022), AWS Certified Database - Specialty: Study Guide with Practice Questions and Labs - Volume 1 of 2: Relational & Managed Database Services: First Edition, Independently published

Carter, P. A, (2022), Pro SQL Server 2022 Administration: A Guide for the Modern DBA, Apress; 3rd ed. Edition

Fritchey, G. (2022), SQL Server 2022 Query Performance Tuning: Troubleshoot and Optimize Query Performance, Apress; 6th ed. edition

DSI ACE PREP, (2022), Data Science Interview: Prep for SQL, Panda, Python, R Language, Machine Learning, DBMS and RDBMS – And More – The Full Data Scientist Interview Handbook, Data Science Interview Books

Clarke, E. (2022), Data Analytics, Data Visualization & Communicating Data: 3 books in 1: Learn the Processes of Data Analytics and Data Science, Create Engaging Data Visualizations, and Present Data Effectively, Kenneth M Fornari

This module covers databases and data science, and the intersection that exists for commercial advantage. The module will examine core elements of data science, practical case studies, before moving into advanced databases. Related to databases the module will address – architectures, design, security, design models, and implementation approaches. In terms of assessment students will create an advanced database solution to a problem, and also investigate future database technologies.