Module Title: Business Data Analytics
Language of Instruction:English
Credits: 10
NFQ Level:8
Module Delivered In 1 programme(s)
Teaching & Learning Strategies: Learners will develop knowledge, understanding and practical skills through labs and workshops with supporting lectures where appropriate. Delivery of technical content will promote discovery learning, where hands-on practical workshops will be utilized to enable learners to apply knowledge and skills, supported by an instructor led, peer learning environment.
Module Aim: The aim of this module is to allow learners to understand foundational skills in data analytics as applied in a business context and to successfully utilise tools to visualize data insights.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Summarize the role and importance of data analytics in business
LO2 Discover and explain the path from data analysis to business action
LO3 Synthesize software tools for business data analysis
LO4 Visualize data and effectively communicate analysis using appropriate technologies
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
 

Module Content & Assessment

Indicative Content
Understanding Data Analytics
The importance of data and data analytics in business, the information lifecycle, practical examples in business environment. Regulatory requirements including GDPR.
Modeling Data
How best to represent your data; designing a database for tabular data (1-N-F); designing an "unstructured" database for complex data; logical models (relational, ER, network, hierarchical, object); structured, semi-structured & unstructured data; pre-defined vs. user-defined data models; tables vs. key/value pairs.
Statistics
Tools from statistics for understanding distributions and probability, hypothesis testing for determining the significance of an observation, and the R system for working with statistical data.
Acquiring, Storing and Managing Data
Data acquisition, data storage, data retrieval, data volume/velocity/variety/veracity. ETL. Brief synopsis of Hadoop and related core technologies through prebuilt appliances (MR, HDFS, Hive, Pig, HBase, Spark).
Data Visualisation
Introduction to the theories underpinning data visualization, best practice in using visualizations effectively, and practical skills in creating visualizations from datasets (e.g. Tableau, D3.js, Einstein).
Assessment Breakdown%
Continuous Assessment60.00%
End of Module Formal Examination40.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Case Studies Review an existing case study and provide insight on the path from data analysis to business insight. 2 20.00 Week 24
Project Perform analysis on a given data set and communicate results via visualisation technologies. 3,4 40.00 Week 26
No Project
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam n/a 1,2 40.00 End-of-Semester

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Laboratory 12 Weeks per Stage 6.00
Independent Learning Time 15 Weeks per Stage 11.87
Total Hours 250.00
Workload: Part Time
Workload Type Frequency Average Weekly Learner Workload
Lecture 12 Weeks per Stage 3.00
Assignment 15 Weeks per Stage 5.93
Total Hours 125.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_KWCCD_B Bachelor of Science (Honours) in Creative Computing and Digital Innovation 8 Mandatory