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Database Technologies for Business Analytics

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.

Full module specification

Module title:Database Technologies for Business Analytics
Module code:BEMM459
Module level:M
Academic year:2022/3
Module lecturers:
  • Dr Juan Rendon-Sanchez - Convenor
Module credit:15
ECTS value:



This module is closed to MSc Business Analytics only



Duration of module: Duration (weeks) - term 2:


Module aims

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);

(c)   Design and implement a NoSQL (non-relational) database (Redis, MongoDB, Neo4j);

(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 )


ILO: Module-specific skills

  • 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

  • 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

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

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity20Lectures (2 hours) x 10
Scheduled Learning and Teaching Activity6Computer lab (1 hour) x 6
Scheduled Learning and Teaching Activities2Tutorials (1 hour) x 2
Guided Independent Study86Reading and preparation for lectures and labs
Guided Independent Study36Preparation of assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Quizzes and exercises during labsIn class1-5Verbal in class

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Group assignment with 3 elements502500 words, group presentation, submission of python code1-5Written
Final Exam501.5 hours1,2,5ELE/Written and verbal where required

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Group assignment (50%)Reassessment coursework 2500 words individual presentation and submission of python code) (50%)1-5Summer reassessment period
Exam (50%)Exam, 1.5 hours (50%)1,2,5Summer reassessment 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%.

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.

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)

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