Module Specifications..
Current Academic Year 2023 - 2024
Please note that this information is subject to change.
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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. | |||||||||||||||||||||||||||||||||||||||||
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 |
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Indicative Content and Learning Activities
Introduction to Machine Learning and Artficial IntelligenceHistory 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 AnalysisText Mining- Text Mining Concepts, Text Mining Metrics, Tagging Metrics - Information Retrieval, Clustering, Tagging and Extraction - Applications, Sentiment Analysis, Opinion Mining - AI and Natural Language ProcessingLinear Regression- Basic Predictive Modelling Principles, Training Data, Variable Selection - Parameter Estimation (Maximum Likelihood) - Model Fit, Model Testing, Validation Data - Advanced Models, Non-Linear Models, OverfittingClassification Models- Introduction to Classification - Logistic Regression Models, Model fit in Logistic regression - Naieve Bayes Classifiers, Decision Trees, Random ForestsMachine Learning- What is Machine Learning, Machine Learning vs Artificial Intelligence - Support Vector Machines - Neural Networks | |||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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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. | |||||||||||||||||||||||||||||||||||||||||
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