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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Mining and Predictive Analytics
Module Code MT413
School DCUBS
Module Co-ordinatorSemester 1: Mathieu Mercadier
Semester 2: Gerry Conyngham
Autumn: Mathieu Mercadier
Module TeachersGerry Conyngham
Mathieu Mercadier
NFQ level 8 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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.

Workload Full-time hours per semester
Type Hours Description
Lecture15Lectures on key concepts and techniques in Predictive Modelling and Data Mining
Workshop15Workshops in Python applying the techniques in module
Independent Study20No Description
Assignment Completion40No Description
Online activity35Onlline Indepedent Learning using Kubicle
Total Workload: 125

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

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentStudents 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
ParticipationParticipation in activities and exercises throughout the modlue10%n/a
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List

  • Albon, C: 2018, Machine Learning with Python Cookbook: Practical solutions from preprocessing to Deep Learning., O’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 – 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.,
  • 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,
Other Resources

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.
Programme or List of Programmes
AFBA in Accounting and Finance
BSBachelor of Business Studies
BSEBachelor of Business Studies (Exchange)
BSIBusiness Studies ( with INTRA )
EBCBA in Global Business (Canada)
EBFBA in Global Business (France)
EBGBA in Global Business (Germany)
EBSBA in Global Business (Spain)
EBTBA in Global Business (USA)
INTBBachelor Business Studies International

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