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

The aim of this module to introduce students to the key data mining and predictive analytics techniques used by modern data driven organisations. Students will be introduced to both supervised and undersupervised learning. Students will shown how to apply all of techniques using Python using data from real world applications.

Learning Outcomes

1. Will be able to explain the role of data mining in modern data driven organisations and apply a variety of techniques to identify patterns in large datasets.
2. Will be able to choose and apply the appropriate predictive analytics techniques for modelling a variety of the key variables and metrics in business.
3. Will be able to choose and apply the appropriate techniques for mining and analysing unstructured datasets including text and network data.
4. Will be able to interpret and effectively communicate the output from a suite of Predictive Analytics models, including the limitations and applications of these models.
5. Will gain a knowledge of the applications of advanced machine learning techniques in business.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	15	Lectures on key concepts and techniques in Predictive Modelling and Data Mining
Workshop	15	Workshops in Python applying the techniques in module
Independent Study	20	No Description
Assignment Completion	40	No Description
Online activity	35	Onlline Indepedent Learning using Kubicle
Total Workload: 125

Section Breakdown
CRN	21143	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Mathieu Mercadier	Module Teacher	Michael Farayola

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Students will be asked demonstrate their ability to build and test predictive models using datasets from a number of business applications.	70%	n/a
Report(s)	Students will choose a Business Sector / Function and outline how predictive analytics can be applied and is being used to forecast future values of key metrics in the sector. Including some specific examples in their report. This report should use both academic and industry literature to support the discussion discussion.	20%	n/a
Participation	Participation in activities and exercises throughout the modlue	10%	n/a

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

Introduction to Machine Learning and Artficial Intelligence
History of AI and ML, Applications, Differences between Standard Programming and Machine Learning, Terminology in Analytics, Overfitting, ...

Data Mining Techniques
- Data Mining Process, Similarity Measures, Predictive Performance - Principal Component Analysis, Supervised Data Mining, K-nearest Neighbours - Unsupervised Data Mining, Hierarchical Cluster Analysis, K Means Cluster Analysis, Association Rule Analysis

Text Mining
- Text Mining Concepts, Text Mining Metrics, Tagging Metrics - Information Retrieval, Clustering, Tagging and Extraction - Applications, Sentiment Analysis, Opinion Mining - AI and Natural Language Processing

Linear Regression
- Basic Predictive Modelling Principles, Training Data, Variable Selection - Parameter Estimation (Maximum Likelihood) - Model Fit, Model Testing, Validation Data - Advanced Models, Non-Linear Models, Overfitting

Classification Models
- Introduction to Classification - Logistic Regression Models, Model fit in Logistic regression - Naieve Bayes Classifiers, Decision Trees, Random Forests

Machine Learning
- What is Machine Learning, Machine Learning vs Artificial Intelligence - Support Vector Machines - Neural Networks

Indicative Reading List

Books:

Albon, C: 2018, Machine Learning with Python Cookbook: Practical solutions from preprocessing to Deep Learning., Oâ€™Reilly Media.,
Hastie, T., Tibshirani, R., Friedman, J.: 2017, The Elements of Statistical Learning: Data Mining, Inference, and Prediction., Springer,
Mathur, P: 2020, Machine Learning Applications using Python â€“ Cases Studies from Healthcare, Retail, and Finance., Apress.,
MÃ¼ller, A., C., Guido, S: 2016, Introduction to Machine Learning with Python: A guide for Data Scientists., Oâ€™Reilly Media.,
Jaggia, Sanjiv: 2021, Business analytics: communicating with numbers, McGraw-Hill, New York,
Michele Chambers, Thomas W. Dinsmore: 2014, Advanced Analytics Methodologies: Driving Business Value with Analytics, Pearson,

Articles:
None

Other Resources

None

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.