Data Analytics - Module
Data Analytics CS5062
The module will cover the following areas
Introduction to data analytics: relation between data mining, data analytics, data science; motivation behind data analytics; cross-industry standard process (CRISP-DM) for data mining; data analytics workflows.
Data pre-processing: feature extraction, data cleaning, handling missing data, methods for identifying outliers, data transformation.
Methods for feature selection: filter, wrapper, and embedded methods.
Styles of machine learning for data mining: supervised vs. unsupervised learning, classification, numeric prediction, clustering, association learning.
Algorithms for building predictive and descriptive analytics models:
Predictive modelling algorithms for classification and numeric prediction, such as OneR, ID3, C4.5, Naïve Bayes, k-NN, Prism, SVM, linear regression, logistic regression, Perceptron, Winnow.
Descriptive modelling algorithms for clustering and association learning, such as k-means, apriori, max-miner.
Evaluation of predictive and descriptive analytics models: Holdout and cross-validation, cost-benefit analysis, user feedback.
Visual analytics: methodology and workflow.
Case studies in subdomains, such as sentiment analysis, item/service ranking recommendation, image classification, etc.
Practical use of data mining platforms for building data mining workflows and training predictive and descriptive analytics models.
Applicants must have a minimum Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline, or a Level 8 Honours degree in other disciplines, which has a significant mathematics and computing element
Successful completion of this module does not automatically qualify you for entry into a further award. All programme applicants must meet the entry requirements listed if applying for a further award.
Please ensure you enter the Module Code below when applying for this MicroCred. Applications without this cannot be processed.
You may apply for more than one MicroCred under the same application.
7 weeks part-time.
Next Intake: Spring 2024
Post Course Info
This micro-credential represents a single module within a larger further award (e.g., Certificate, Diploma, Masters). By taking this micro-credential you may be eligible to apply for a credit exemption should you progress to study for a further award.
The programme(s) associated with this MicroCred are:
MSc in Artificial Intelligence