Module Code - Title:
MG4077
-
DATA CURATION FOR BUSINESS ANALYTICS
Year Last Offered:
N/A
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
Rationale and Purpose of the Module:
This module introduces students to the principles and practices of preparing data for effective use in business analytics. It focuses on the end-to-end process of data curation, including sourcing, cleaning, integrating, transforming, and validating data to support analytical tasks. Students will examine key issues such as data quality, consistency, and ethical data management. The module incorporates practical experience with tools and scripting languages commonly used in industry (e.g., SQL, Python), enabling students to work with real-world datasets and prepare them for visualisation, analysis, predictive modelling, reporting, and data-driven decision-making in contemporary, digitally transformed business environments.
Syllabus:
Identifying and developing business datasets; data cleaning; handling missing values and outliers; understanding and modifying data types; combining data; summarising, aggregrating and grouping business data; filtering and querying for managerial decision making; outlining business cases; visualising and reporting on business insights.
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
Modify datasets for analysis.
Transform business data for reporting purposes.
Filter and query data for managerial decision making.
Visualise large datasets for problem-solving.
Report on insights gained.
Affective (Attitudes and Values)
On successful completion of this module, student will be able to:
Prioritise ethical, responsible, and high‑quality data practices by upholding rigorous data‑curation standards in applied analytical work.
Psychomotor (Physical Skills)
N/A
How the Module will be Taught and what will be the Learning Experiences of the Students:
The module will be taught through a combination of lectures and labs. The module will focus on sourcing and cleansing datasets for business purposes. Students will lean how to modify large datasets to answer complex questions and support managerial decision making. Students will be expected to proactively manage their learning experience and develop skills in independent learning. Relevant, up to date research on data wrangling and data curation techniques will be integrated into the module. The module will be assessed using a range of tools including lab based assessments which will assess students practical and problem solving skills.
Prime Texts:
Agal S (2025)
Data Wrangling and Preprocessing
, Notion Press
Other Relevant Texts:
Seefeld, K. (2025)
Data Wrangling: Essential Concepts for Analytics
, Data Analytics Curriculum.
Programme(s) in which this Module is Offered:
BACHELOR OF BUSINESS STUDIES
Semester(s) Module is Offered:
Autumn
Module Leader:
michaelp.obrien@ul.ie