Long Title:Introduction to Data Analytics
Module Code:H8IDA
Credits: 10
NFQ Level:LEVEL 8
Field of Study: Software and applications development and analysis
Module Delivered in 1 programme(s)
Module Coordinator: EUGENE O'LOUGHLIN
Module editor: Helen Power
Module Description: The aim of the course is to teach learners the techniques for storing data in a way that optimises the retrieval and throughput for data analytics for business domains. This includes learners developing practical skills and gaining conceptual knowledge about different database technology paradigms and how these technologies can be used to store, maintain, retrieve and analyse large datasets. Specifically, this module’s objectives include understanding and using relational databases (including advanced SQL concepts), non-relational databases (e.g., NoSQL key/value databases), data warehousing techniques and cloud computing data storage options.
Learning Outcomes
On successful completion of this module the learner will be able to:
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
Pre-requisite learning
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
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 

Module Content & Assessment

Indicative Content
1. Databases and Storage (25%)
• Collecting Data • Data Storage • Data Modelling • Normalisation • Indexes • Relational Databases • DBMS File Management • Tuning at the Internal level • Indexing and Hashing • Query processing and optimisation • Database Performance Evaluation
2. SQL for Data Retrieval (30%)
• Outputting Data Streams • Complex Joins/Multi-Joins • Sub/Correlated Queries • Views • Integrity Enhancement Features of SQL • Advanced Data Definition
3. Non-relational Databases (10%)
• Types of non-relational databases • Storing and retrieving information • Algorithmic based queries • Distributed data storage • Cloud-based data storage
4. Data Warehousing (35%)
• Introduction to Data Warehousing • Data Warehousing Concepts • Types of Data Warehouse • Designing a Data Warehouse Database • Building a Data Warehouse • Using a Data Warehouse • On-line analytical processing (OLAP) • Data-mining • Administering a Data Warehouse • Challenges of Data Warehousing
Learning Environment
Learning will take place in classroom or lab environments as appropriate. In lab environments, each student will have access to a PC with a database. Learners will have access to library resources and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the college’s LMS. Labs The labs will concentrate on implementing and manipulating data for analysis, and how best to implement the theory learned during the module.
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Written Report Learners must prepare a literary review and analysis covering specific optimisation techniques applied by corporate database vendors. 1,2,3,4,5 25.00 n/a
Practical (0260) a. Learners will be presented with an organisations data requirement and expected output objectives, designed to cover the range of data storage and retrieval functions on the syllabus. b. From this information, learners will be required to design and implement a data model complete with large amounts of data. From this create a data warehouse model and provide the complete reporting data set. (25%). ** It should be noted that learners can use their own predefined datasets to create the data warehouse for practical assessment as this may be advantageous to the learning. 1,2,3,4,5 25.00 n/a
End of Module Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Terminal Exam End-of-Semester Final Examination 1,2,3,4,5 50.00 End-of-Semester
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

This module has no Full Time workload.
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Tutorial No Description 2 Every Week 2.00
Total Hours 4.00
Total Weekly Learner Workload 4.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Thomas M. Connolly, Carolyn E. Begg, Database systems, Fifth Edition Ed., Boston ; Addison-Wesley, c2010. [ISBN: 0321523067]
Supplementary Book Resources
  • Gordon S. Linoff, Data Analysis Using SQL and Excel, Wiley [ISBN: 0470099518]
  • Eric Redmond, Jim Wilson, Seven Databases in Seven Weeks, Pragmatic Bookshelf [ISBN: 1934356921]
  • Baron Schwartz, Peter Zaitsev, Vadim Tkachenko, High Performance MySQL, O'Reilly Media [ISBN: 1449314287]
This module does not have any article/paper resources
Other Resources
  • Website: http://www.thearling.com
  • Website: http://www.mongodb.org
  • Website: http://www.mysql.com
 

Module Delivered in

Programme Code Programme Semester Delivery
HDSDA Higher Diploma in Science in Data Analytics 1 Core Subject