Module Code: A8DM
Long Title Data Management
Title Data Management
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: EAMON NOLAN
Module Author: Madita Feldberger
Departments: School of Computing
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Capture requirements for appropriate data storage technologies
LO2 Design and Implement effective data models
LO3 Investigate and implement dataset pre-processing techniques
LO4 Investigate and utilise relational and non-relational databases for optimised storage, retrieval, and organisation of data
LO5 Use data warehousing and online analytical processing techniques to create Dashboards for Data Visualisation
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
Module Introduction Databases & Storage
Overview of a Database, Functions of a DBMS, Advantages & Disadvantages of DBMS’s
Databases & Storage
The Relational Model, Properties of database relations, How to identify CK, PK, and FKs, Purpose and advantages of views
Database Design ERD
Relationship (ER) modelling in the database design, Basic concepts associated with ER model, Diagrammatic technique for displaying ER model using Chen or Crows Foot (Barker), ERD Modelling Cont’d
Database Design NF
How normalisation can be used when designing a relational database. The potential problems associated with redundant data in the base relations. The concept of functional dependency, which describes the relationship between attributes.
SQL for Data Retrieval
DDL & DML 1. Data Types 2. DDL Commands 3. SQL Exercises, How to retrieve data from database using SELECT, WHERE & ORDER BY. Use AGGREGATE functions. Group data using GROUP BY and HAVING. Subqueries / Table Joins Perform set operations (UNION, INTERSECT, EXCEPT). Stored Procedures/Triggers
Indexing/Performance Tuning
Single-level Ordered Indexes, Multi-level Indexes, B-Trees and B+Trees
Data Warehousing
The main concepts and benefits associated with data warehousing. The problems associated with data warehousing. The tools associated with data warehousing. The concept of a data mart and the main reasons for implementing a data mart. Two main methodologies for the development of a data warehouse Kimball’s Business Dimensional Lifecycle Inmon’s Corporate Information Factory (CIF). The step-by-step creation of a dimensional model (DM) using their previously created Database and some other case studies. Dashboards – Graphical Visualisation of Data
Non-Relational Databases
Types of non-relational databases, Storing and retrieving information, Algorithmic based queries, Distributed data storage
Distributed Databases
Distributed data storage Advantages & Disadvantages, Types of DDBMSs, Distributed Relational Database Design, Transparencies in a DDBMS, DDBMS Functions & Architecture
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Assessments

Full Time

Coursework
Assessment Type: CA 1 (0380) % of total: 25
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
Apprentices will be asked to Design, implement and populate a real life Corporate Database of their choice that fulfils a real business need
Assessment Type: CA 2 (0390) % of total: 25
Assessment Date: n/a Outcome addressed: 3,4,5
Non-Marked: No
Assessment Description:
Using the Database that was created earlier, apprentices are now requested to design, implement and populate a real life Data Warehouse to support the reporting requirements of the company. The data warehouse should support at least 1 Financial Data Mart. The results of this Ca should be displayed on an online dashboard.
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Weekly tutorials will provide opportunities for both one to one feedback from the lecturer as well as peer review and feedback.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
The examination will be in the region of two hours in duration and may include a mix of: short answer questions, essay based questions and case study based questions. Marks will be awarded based on clarity, appropriate structure, relevant examples, depth of topic knowledge, and evidence of outside core text reading.
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
 

Module Resources

Recommended Book Resources
  • Thomas Connolly, Carolyn Begg. (2014), Database Systems: A Practical Approach to Design, Implementation, and Management, 6th Edition. Pearson Education.
  • Baron Schwartz, Peter Zaitsev, Vadim Tkachenko. High Performance MySQL, O'Reilly Media.
Supplementary Book Resources
  • Gordon S. Linoff. Data Analysis Using SQL and Excel, Wiley.
  • Redmond E., Wilson J.R.. (2009), Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement, O’Reilly Media.
This module does not have any article/paper resources
Other Resources
Discussion Note: