Module: Data Analytics: Programming & Visualisation

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Analytics: Programming & Visualisation
Module Code	BAA1026 (ITS: MT412)
Faculty	DCU Business School	School	DCU Business School
NFQ level	8	Credit Rating	10

Description

The aim of this course focusing on programming (SQL, Python) and visualisation (Power BI) for business analytics is to provide the skills needed to manage, analyse, and present data effectively. Mastering these three areas helps to handle the complete data analysis pipeline, from extraction and analysis to visualisation, enabling to make data-driven business decisions and present insights clearly to stakeholders.

Learning Outcomes

1. Will understand the concepts of primary/foreign keys, of the three normal forms, and be able to design and interpret Entity-Relationship Diagrams (ERD) to model database structures effectively.
2. Will be proficient in using SQL commands such as SELECT, INSERT, UPDATE, and DELETE, along with join clauses, to retrieve, manipulate, and integrate data from multiple tables within a database.
3. Will be able to create interactive dashboards and reports that effectively visualise business data, selecting the appropriate visualisations to best represent different types of data and insights.
4. Will develop collaborative skills in data storytelling and presentation by delivering a team-based Power BI project, learning how to design Power BI reports that clearly communicate key insights and drive data-driven decision-making among stakeholders.
5. Will understand and use basic Python programming concepts, such as variables, loops, and functions, to write scripts for automating data processing tasks.
6. Will learn to utilise NumPy and Pandas libraries to efficiently manipulate, clean, and analyse data, enabling them to perform data transformations, aggregations, and basic statistical computations.
7. Will develop the ability to create visual representations of data using Matplotlib, enabling them to generate plots, charts, and graphs that effectively communicate analytical insights.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	22	In class lectures
Workshop	22	In class workshops on SQL, Power BI, and Python were students collaborate to solve analytics programming challenges to develop their Analytics skills
Tutorial	12	Tutorials for further practice in SQL, Power BI, and Python
Independent Study	144	Independent study
Online activity	50	Using online resource to learn Python, Power BI and SQL
Total Workload: 250

Section Breakdown
CRN	11814	Part of Term	Semester 1
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Mathieu Mercadier	Module Teacher	Babu Veeresh Thummadi

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	SQL Assessment	30%	Week 6
Group presentation	Power BI project preparation and presentation	30%	Week 9
In Class Test	Python Programming Assessment	40%	Week 12

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

SQL
Learn to use SQL to extract and manage data from relational databases. Develop skills in writing queries, ERD, managing large datasets, performing data joins, and optimising database operations, all crucial for preparing data for analysis.

Power BI
Power BI for creating interactive reports, dashboards, and meaningful data visualisations. The focus is on presenting data in a user-friendly way, using visual storytelling to communicate insights effectively.

Python
Introduction to Python programming for data manipulation, analysis, and automation. Learn to use libraries like NumPy for numerical computations, Pandas for data handling, and Matplotlib for basic visualisations. These skills help automate data processing tasks and apply advanced analytics techniques, such as machine learning models (see in BAA1027, semester 2), to gain deeper insights.

Indicative Reading List

Books:

Behrman, K.: 2022, Foundational Python for Data Science, 1, Addison Wesley,
Camm, J., D., Cochran, J., J., Fry, M., J., Ohlmann, J., W.: 2023, Business Analytics, Cengage,
Evans, J., R.: 2020, Business Analytics, Pearson,
Fenner, M.: 2020, Machine Learning with Python for Everyone, Addison Wesley Data & Analytics Series. Pearson,
Lambert, K.: 2019, Fundamentals of Python: Data Structures, Cengage,
Liang, Y., D.: 2023, Introduction to Python Programming and Data Structures, Pearson,
Mathur, P.: 2019, Machine Learning Applications using Python – Cases Studies from Healthcare, Retail, and Finance, APress,
Müller, A., C., Guido, S.: 2016, Introduction to Machine Learning with Python: A guide for Data Scientists, O’Reilly Media,
O’Connor, E.: 2018, Microsoft Power BI Dashboards Step by Step, Pearson,
Sharda, R., Delen, D., Turban, E.: 2021, Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Pearson,
Shellman, M., Afyoundi, H., Pratt, P., J., Last, M., Z.: 2023, A guide to SQL, Cengage,
Stephens, R.: 2022, SQL in 24 Hours, Sams Teach Yourself, Pearson,

Articles:
None

Other Resources

1: Online Training, Kubicle, Online Training in MS EXCEL, POWER BI, SQL, PYTHON, Machine Learning and AI,

This is a 10 Credit Module which is part of the 20 Credit Specialism in Business Analytics. All three moduels are corequisites