Data Analytics & Prediction (L9, 10 ECTS)

1. Introduction to Data Analytics and Data Management

• Key concepts and principles of data analytics
• Data lifecycle and governance in engineering
• Data quality, integrity, and management practices

2. Statistical Methods and Tools

• Descriptive and inferential statistics
• Probability distributions and hypothesis testing
• Statistical software tools (e.g., MATLAB, R, Python)

3. Multivariate Analysis Techniques

• Principal Component Analysis (PCA)
• Factor Analysis
• Cluster Analysis
• Applications in mechanical engineering

4. Prediction Modelling

• Regression analysis (linear and nonlinear)
• Time series analysis and forecasting
• Machine learning algorithms for prediction (e.g., decision trees, neural networks)
• Model validation and performance metrics

5. Continuous Improvement and Sustainability

• Introduction to continuous improvement techniques and their relevance in engineering
• Tools for continuous improvement (e.g., Pareto charts, cause-and-effect diagrams, control charts)
• Using data analytics to support sustainable engineering practices
• Case studies on resource efficiency and environmental impact reduction

6. Data-Driven Decision Making in Mechanical Engineering

• Case studies of data analytics in mechanical engineering
• Developing data-driven strategies for process optimization
• Communicating data insights to stakeholders

7. Ethical, Legal, and Environmental Considerations

• Data privacy and security in engineering projects
• Ethical implications of data analytics and prediction modelling
• Legal requirements and compliance
• Environmental responsibility in data-driven engineering solutions

A minimum Second Class Honours, Grade 2 (H2.2), in a Level 8 Mechanical Engineering degree OR related engineering discipline, or equivalent, from a recognised university or third-level college, or international equivalent engineering programme (as described above).

or

Any qualification(s) deemed by the TUS as being equivalent to point 1. above, when taken in conjunction with Recognised Prior Learning (RPL) and/or relevant work experience.

Factors taken into account in determining admission will include the specific content of the undergraduate degree, the applicant’s performance, and the availability of places.

or

Recognised Prior Learning (RPL) 
If you do not meet the academic entry requirements listed above but would like your application assessed under RPL please select YES on the online application form. Please include any relevant work experience/further information relevant to your application under the ‘Additional Information’ section.

English Language: Applicants who do not have English as their first language must ensure they satisfy English Language requirements. For entry to undergraduate courses, a score of 5.5 in an IELTS or equivalent exam is required. For postgraduate courses, a score of 6.0 in an IELTS or equivalent exam is required. It is the responsibility of the applicant to ensure their English proficiency meets these requirements. Please note that we do not request proof of English language qualification or proficiency.

The programme will run for 15 Weeks, 1 evening per week, 4 hours per evening

Mondays from 6pm to 10pm.

The proposed delivery schedule is subject to change.

Each 5 credits will normally equate to approximately 100 Total Learning Hours. Total Learning Hours includes the time you spend in class (lectures, tutorials, practical elements) and the time you spend completing work outside of college.  The balance between these two varies by discipline, and by level of study. You should bear in mind that the workload will increase at particular times e.g. when assignments are due.

100% Continuous Assessment

Special Purpose Award

Certificate in Mechanical Engineering in Data Analytics and Prediction Modelling (Level 9, 10 ECTS)

This course is funded by the Micro-credential Learner Fee Subsidy 2026 programme and fees are subsidised at 80% for all eligible learner categories.

Full Fee is € 1,420

General Queries

Flexible Learning Office

Email: flexible.midwest@tus.ie

Telephone: (061) 293802

Academic Queries

Dr. Daniela Butan

Email: daniela.butan@tus.ie