Professional learners in engineering sector who wants to acquire fundamental skills to effectively engage in and manage data analytics projects and develop their data analysis skills and competencies.
Industrial Data Analytics
Level 9 | 5 ECTS | Sept 2024 | € 875 | 80% Learner Fee Subsidy
Overview
| Module Code | MEEN41380 |
| Module Title | Industrial Data Analytics |
| Subject Area | Digital Manufacturing/Engineering |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | September 2025 |
| Duration | 12 weeks |
| Mode of Delivery | Online |
| Course Leader | Dr. Javad Zeinali |
| Fee | Full Fee: €900 |
| Application Deadline | September 2025 |
The aim of this micro-credential is to answer the questions on how to develop an appropriate data analytics solution for an industry challenge, and what predictive model is the best choice of this solution.
In addition, it provides a unique opportunity to gain insights into the fundamental concepts, techniques, and applications of data analytics in an industrial environment. An important objective is to explain key steps involved in a predictive data analytics project. The course does not involve coding; however, easy-to-use choices are available for practitioners who are interested.
Dr Javad Zeinali, Assistant Professor of Digital Manufacturing in the School of Mechanical and Materials Engineering at University College Dublin discuss the content of Industrial Data Analytics micro- credential. This course is designed for professional learners to acquire fundamental skills necessary to effectively engage in and manage data analytics projects and machine learning. An important objective of this course is to explain key steps involved in a predictive data analytics project. Offered fully online it offers flexible options for busy professionals.
On successful completion of this micro-credential, you will be able to:
- Provide data analytics solutions to address industry data-related challenges.
- Select the most appropriate data analytics approaches tailored to the available dataset and project challenge.
- Analyse real-time and historical data.
- Define main steps needed in the data preprocessing.
- Demonstrate an in-depth understanding of predictive data analytics lifecycle and stages.
- Demonstrate an understanding of different data analytics approaches.
Topics to be covered include:
- Introduction to Industrial Data Analytics
- Predictive Data Analytics Project Lifecycle: CRISP-DM
- Business and Data Understanding
- Data Preparation and Visualisation
- Machine Learning Fundamentals
- Model Evaluation and Deployment
Learners will acquire fundamental skills to effectively engage in and manage data analytics projects. Learners will also develop their data analysis, predictive analytics and problem-solving skills and competencies.
This micro-credential is delivered through the UCD online learning platform (Brightspace) and will consist of lectures/seminars, critical writing, and learner presentations.
There will be a two-hour live session each week, and a pre-recorded video will be made available for those who are unable to attend. The course includes a series of activities, such as article reviews, designed to help learners identify a solution for a business or industry challenge of their choice. These activities are intended to support their assignments. Three assignments are designed for learners:
The first assignment is developing a data analytics solution for their chosen business or industry challenge; The second assignment requires them to review a peer-reviewed article related to their challenge; And the final assignment is to prepare a project report or presentation detailing the solution they proposed in the first assignment.
This is a 5 credit micro-credential and involves approximately 120 hours of learner effort.
Entry requirements are a primary degree in (engineering, technology, mathematics or science) with a minimum of
2.2 classification (second class honours , grade two) or International equivalent.
Applicants whose first language is not English must also demonstrate English language proficiency of IELTS 7.0 (no band less than 6.5 in each element), or equivalent.
- Article review – 20%
- Solution overview – 30%
- Project report or presentation – 50%
Written feedback will be provided for essays. Group/class feedback will be provided for presentations.
ME Management PT (T368)
Industrial Data Analytics MEEN41380
Decision AnalysisMEEN40360
Engineering Operations Management MEEN40260
If you have any questions about this micro-credential, or would like to speak to a UCD staff member, please contact (opens in a new window)microcredentials@ucd.ie.