Course Details

Course Code(s):
CS5062
Available:
Part-Time
Intake:
Spring
Course Start Date:
Spring 2025
Duration:
7 Weeks
Award:
University Certificate of Study
Qualification:
NFQ Level 9 Minor Award
Faculty: Science and Engineering
Course Type: Professional/Flexible, Online
Fees: For Information on Fees, see section below.
Application Deadline:

Contact(s):

Name: SEFLC
Address: Science & Engineering Flexible Learning Centre Email: seflc@ul.ie
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Brief Description

This module introduces the elements of the data analytics workflow, including data cleaning, feature extraction and feature selection, predictive and descriptive modelling, and deployment of models. The role of data visualisation in the data analytics process is discussed as well. The module involves practice with a state-of-the-art data analytics software platform.

FUNDING

This course qualifies for 80% funding under the HCI Micro-Credential Course Learner Subsidy. Check fees section for details and eligibility. Applications for Spring 2025 will open in October, express your interest to be notified when the course opens for application. Please Note: Applicants may only apply for and receive, one subsidised course per semester.

APPLICATION INFORMATION

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.

Course commencement is subject to minimum numbers.

PATHWAY

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: 

This module introduces the elements of the data analytics workflow, including data cleaning, feature extraction and feature selection, predictive and descriptive modelling, and deployment of models. The role of data visualisation in the data analytics process is discussed as well. The module involves practice with a state-of-the-art data analytics software platform.

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.

On successful completion of this module students will be able to:

  • Summarise the main elements of the data mining workflow.
  • Differentiate predictive from descriptive analytics in terms of methods and output.
  • Recognise and describe at least one algorithm in each of the four categories: classification, numeric prediction, clustering, association learning.
  • Construct data mining workflows with the use of data mining software for training of predictive and descriptive analytics models.
  • Analyse the results of machine learning algorithms.
  • Recognise the role of data visualisation in the data mining process.
  • Discuss the benefits of data mining for industry and society.

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.

Applicants are expected to have completed a basic introduction to Python.

Entry requirements are established to ensure the learner can engage with the course material and assessments, at a level suitable to their needs, and the academic requirements of the module. By applying to this micro-credential, you are confirming that you have reviewed and understand any such requirements, and that you meet the eligibility criteria for admission.

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.

The fees for this programme are €1,000

HCI Micro-Credential Course Learner Subsidy - Candidates who satisfy the eligibility criteria can qualify for 80% funding subject to the availability of places. To clarify eligibility please go to Eligibility Criteria

Please click here for information on funding and scholarships.