Module: Analytics Tools and Programming

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Analytics Tools and Programming
Module Code	BAA1088 (ITS: SB5001)
Faculty	DCU Business School	School	DCU Business School
NFQ level	9	Credit Rating	10

Description

This module focuses on programming (SQL and Python) for business analytics and machine learning, and it equips students to manage, analyse, and present data effectively. Mastering these skills helps to handle the complete business analytics pipeline, from extraction and analysis to visualisation, enabling to make data-driven business decisions and present insights clearly to stakeholders.

Learning Outcomes

1. 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. Use 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. Understand and use basic Python programming concepts, such as variables, loops, and functions, to write scripts for automating data processing tasks.
4. Use NumPy and Pandas libraries to efficiently manipulate, clean, and analyse data, enabling him/her to perform data transformations, aggregations, and basic statistical computations.
5. Create visual representations of data using Matplotlib, enabling him/her to generate plots, charts, and graphs that effectively communicate analytical insights.
6. Understand the fundamentals of Machine Learning, learning concepts such as bias-variance trade-off, overfitting, evaluation metrics, etc.
7. Use the basic functionalities of Scikit-learn for classical Machine Learning (e.g., logistic regression, decision trees, random forest, SVM, ANN, etc.)
8. Understand the ethical implications of data collection, storage, and use, particularly in relation to personal data, transparency, algorithmic bias, and regulatory compliance (e.g., GDPR).
9. Develop collaborative project skills by working in teams to deliver an end-to-end business analytics pipeline, reflecting the cross-functional coordination required to produce reliable, stakeholder-ready insights in real analytics projects.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	44	Lectures + Workshops/Tutorials
Tutorial	11	Additional Tutorials
Independent Study	100	Preparation for, and reading after the lectures
Online activity	45	Online training using Kubicle
Assignment Completion	50	Group assignment work: research and completion
Total Workload: 250

Section Breakdown
CRN	12267	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

Type	Description	% of total	Assessment Date
Assessment Breakdown
Group project	Business Analytics Project Find a real-world company dataset. Using a challenge-based learning (CBL) framework, build an end-to-end business analytics pipeline that explicitly ties the data and model to a concrete operational decision, a named KPI, and a responsible deployment plan. The work must cover EDA, cleaning, preprocessing, feature engineering, a justified train-test split, all must align to the business question. Then, select and train relevant machine learning algorithms. Assess the error, compare models fairly, and translate results into actionable business insights. Include a brief UN SDG and ethics/GDPR	30%	Week 12
In Class Test	SQL Assessment	30%	Week 6
In Class Test	Python 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.

Python
Introduction to Python programming for data manipulation, analysis, and automation. Learn to use libraries like NumPy for numerical computations, Pandas for data handling, Matplotlib for basic visualisations, and Scikit-learn for machine learning. These skills help automate data processing tasks and apply advanced analytics techniques, such as machine learning models (see in BAA1053, semester 2), to gain deeper insights.

Business Analytics and Machine Learning
Introduction to Business Analytics, Data, Visualisation, and Machine Learning. The focus is on introducing core concepts, terminology, and the end-to-end analytics/ML workflow: problem framing, data preparation, visualisation, modelling, evaluation, and communication.

Indicative Reading List

Books:

IIBA: 2015, A Guide to the Business Analysis Body of Knowledge, 5, 6, 8, 10, 11, 978-1-927584
Behrman, K.: 2022, Foundational Python for Data Science, 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, 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,
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 Tutorial, Kubicle, SQL, Python Fundamentals, Machine Learning with Python, Kubicle