Database Technologies for Business Analytics

Module title	Database Technologies for Business Analytics
Module code	BEMM459
Academic year	2024/5
Credits	15
Module staff	Professor Nav Mustafee (Convenor)

Duration: Term	1	2	3
Duration: Weeks		10

Number students taking module (anticipated)	60

Module description

In this module you will learn the basics of database design and how to manage data. You will learn how to use Python to access, manipulate and store data. You will develop a theoretical understanding of relational databases (RDBMS) and NoSQL databases. You will gain practical experience of using Structured Query Language (SQL), using python libraries for data access and data storage, and the use of object database mappers for both RDBMS and NoSQL databases.

Module aims - intentions of the module

This module aims to equip you with both the theoretical knowledge and the practical skills required to:

(a) Design and implement a relational database (Entity-Relationship diagrams, normalisation, implementation using SQLite);

(b) Use Data Query Language with relational databases - data definition language (DDL), data manipulation language (DML) and structured query language (SQL);

(d) Use Python libraries to access relational databases and NoSQL databases;

(e) Learn the use of object mapper libraries for use with databases (object relational mapping, object document mapping and object graph mapping libraries like SQLAlchemy and MongoEngine )

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

1. P1: Demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools.
2. P5: Create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

3. P6: Critically analyse the use of data within a business context, identifying strengths and limitations
4. P7: Critically analyse and interpret relevant academic, technical and industry literature.

ILO: Personal and key skills

On successfully completing the module you will be able to...

5. P14: Technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.

Syllabus plan

The following content will be covered during the course:

Introduction to relational and non-relational databases (theory)
Designing and developing relational and non-relational databases
Entity-relationship modelling and normalisation (relational databases).
Introduction to database languages – Data Definition Language (DDL), Data Query Language (DQL), Data Manipulation Language (DML)
Using Structured Query Language (SQL)
Using Python Libraries for working with databases
Using Python object mapper libraries to access both RDBMS and NoSQL databases.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
28	122	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activity	20	Lectures (2 hours) x 10
Scheduled Learning and Teaching Activity	6	Computer lab (1 hour) x 6
Scheduled Learning and Teaching Activities	2	Tutorials (1 hour) x 2
Guided Independent Study	86	Reading and preparation for lectures and labs
Guided Independent Study	36	Preparation of assessments

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Quizzes and exercises during labs	In class	1-5	Verbal in class

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
50	50	0

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Group assignment with 3 elements	50	2500 words, group presentation, submission of python code (50%)	1-5	Written
Examination	50	1 hour	1,2,5	ELE/Written and verbal where required

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Group assignment (50%)	Reassessment coursework 2500 words individual presentation and submission of python code) (50%)	1-5	Referral/Deferral Period
Examination (50%)	Exam, 1 hour (50%)	1,2,5	Referral/Deferral period

Re-assessment notes

Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

Indicative learning resources - Basic reading

The following text will be referred to throughout the course:

Connolly, T., Begg, C. E., & Holowczak, R. (2008). Business Database Systems. Pearson Education.

Sullivan, D. (2015). NoSQL for Mere Mortals. Addison-Wesley Professional.

Perkins, L., Redmond, E., & Wilson, J. (2018). Seven databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement. 2nd Edition. Pragmatic Bookshelf.

Python code (Tutor’s GitHub repository)

Key words search

Relational Databases, NoSQL, Python, SQL

Credit value	15
Module ECTS	7.5
Module pre-requisites	This module is closed to MSc Business Analytics only
Module co-requisites	N/A
NQF level (module)	7
Available as distance learning?	No
Origin date	09/01/2020
Last revision date	21/02/2024