Module: Data Analytics: Machine Learning & Advanced Python

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Analytics: Machine Learning & Advanced Python
Module Code	BAA1027 (ITS: MT413)
Faculty	DCU Business School	School	DCU Business School
NFQ level	8	Credit Rating	5

Description

This module introduces students to foundational concepts in data analysis, statistics, and machine learning. It aims to develop students’ understanding of how data is represented, analysed, modelled, and interpreted in modern business and data-driven environments. Students will learn key machine learning algorithms, evaluation techniques, and statistical reasoning necessary for building predictive models and making informed analytical decisions. Python is the main programming language used in this module.

Learning Outcomes

1. Describe and distinguish between different data types, data structures, and methods of data representation.
2. Apply descriptive and inferential statistical techniques to summarise, analyse, and interpret datasets.
3. Explain core machine learning concepts including supervised vs. unsupervised learning, model evaluation, cross-validation, and the bias–variance trade-off.
4. Construct and interpret simple predictive models using algorithms such as K-Nearest Neighbours, Decision Trees, Support Vector Machines, and Ensemble Methods.
5. Use evaluation metrics (accuracy, precision, recall, MAE, MSE, RMSE, etc.) to compare model performance and justify model selection.
6. Perform basic clustering and dimensionality-reduction techniques such as K-Means and Principal Component Analysis and interpret their outcomes.
7. Communicate analytical findings clearly using appropriate statistical and machine learning terminology.
8. Explain the structure and operation of Artificial Neural Networks, including perceptrons, activation functions, and multi-layer architectures.
9. Describe common applications of deep learning, such as image recognition, NLP, finance, and recommendation systems.
10. Evaluate the suitability of deep learning approaches for different types of predictive modelling tasks.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	22	Lectures + Workshop
Tutorial	11	Additional Tutorial
Assignment Completion	20	Assignment work: research and completion
Independent Study	72	Preparation for, and reading after the lectures
Total Workload: 125

Section Breakdown
CRN	21143	Part of Term	Semester 2
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	Michael Farayola

Type	Description	% of total	Assessment Date
Assessment Breakdown
In Class Test	Theory-based	20%	Week 6
In Class Test	Theory-based	20%	Week 11
Assignment	Technical Implementation and Scientific reports	60%	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

Data Foundations
• Structured vs. unstructured data • Labeled vs. unlabeled datasets • Numerical, categorical, ordinal, and binary data • Feature representation and encoding

Model Evaluation & Performance
• Confusion matrix • Precision, recall, accuracy, error • Regression metrics (MAE, MSE, RMSE)

Descriptive & Inferential Statistics
• Central tendency • Variance • Hypothesis testing

Correlation & Regression
• Correlation analysis • Linear regression • Limitations and interpretation

Machine Learning Concepts
• Supervised vs. unsupervised learning • Train–test split • Cross-validation • Hyperparameter tuning

Core Algorithms
• K-Nearest Neighbours • Decision Trees • Support Vector Machines • Ensemble Methods

Unsupervised Learning
• K-Means Clustering • Principal Component Analysis

Deep Learning
• Biological Inspiration and Neuron Models • Perceptron and activation functions • Multi-layer feedforward networks (ANNs) • Backpropagation and regularization • Comparison with classical ML approaches

Indicative Reading List

Books:

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, New York,
Raschka, S., Liu, Y., Mirjalili, V., Dzhulgakov D.: 2022, Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python, Packt,
Sharda, R., Delen, D., Turban, E.: 2021, Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Pearson,

Articles:
None

Other Resources

1: Website, Kubicle-Machine Learning with Python,

This Module "Data Mining and Predictive Analytics" is 5 Credtis of the 20 Credits Business Analytics Specialism. All modules are co-requisites. Student choosing this specialism must take all 20 Credits.