Data Science for Manufacturing
This hybrid, credit-bearing course provides both a grounding in the technologies of data science and their potential uses in manufacturing enterprises. Through weekly lectures and hands-on computer workshops students will be introduced to the python programming language and apply some of the tools it provides for manipulating and analysing various forms of data that are common to manufacturing businesses.
The course will deliver all topics via a common platform established by introducing students to the fundamentals of using the Python programming language within Jupyter Notebooks and concepts of software version control with Git and Github. Each topics will be taught via Python libraries whose functionality will enable students to explore their application using high-level commands and a number of curated datasets.
The lectures will introduce and demonstrate concepts and tools that will provide a starting point for workshop exercises that will provide hands-on experience of both the challenges and capabilities of modern digital technologies.
The course will employ case study materials, video resources, and industrial guest speakers to emphasis the industrial relevance of the techniques. Although the focus is on manufacturing, the course will also touch on the philosophies of Product Lifecycle Management and Product Data Management and examines the interactions between information technologies, organisation and product data. It aims to equip participants with the skills to use data science to uncover insight, support informed decision-making and the creation of value from data.
- Week 1: Introduction and Foundations: In the first lecture will explain how the course will work, give a brief overview of Data Science and its application to manufacturing. In the workshop, you will be introduced to: Key data science concepts, Python programming with Jupyter Notebooks, Version control with GIT
- Week 2: Data carpentry: In this lecture you will be introduced to the concepts and tools of data carpentry. The workshop we will discuss and practice with data cleansing and data carpentry.
- Week 3: Product Lifecycle / Material flow: This lecture will discuss data associated with product lifecycle and material flows in manufacturing plants. The need for manufacturing engineers to develop coding skills will be demonstrated using industrial case studies. The workshop we will discuss and practice with Python on Jupyter notebooks and Github.
- Week 4: Data visualization and Exploratory Data Analysis: This lecture overviews and introduces data visualisation formats and techniques and how to perform exploratory data analysis. The workshop we will discuss and practice with data visualisation exploratory data analysis.
- Week 5: Current Manufacturing Software / PLM / ERP /MES: This lecture overviews and introduces current manufacturing software / PLM / ERP /MES. The workshop we will discuss and practice wit data representation and relational databases.
- Week 6: Machine Learning and Artificial Intelligence (ML/AI): This lecture overviews and introduces machine learning and artificial intelligence in the context of manufacturing. The workshop we will discuss and practice with Jupyter Notebooks + ML (vision based exercise).
- Week 7: Asset Management / internet of things (IoT): This lecture overviews and introduces asset management and internet of things. In the workshop you will practice with asset data.
- Week 8: EBoM / MBoM / Geometry / Time Series: This lecture overviews and introduces data representation / EBoM / MBoM / Geometry / Time Series The workshop we will discuss and practice with data representation and relational databases with us and the peers.
- Week 9: Data for Industry 4 / New Business Models / Digital Twin: This lecture overviews and introduces data for the Industry 4.0, new business models, the digital twin and thread. The workshop we will discuss and practice with presenting information with us and the peers.
- Week 10: Assessment: Submitting your final project.
This course is designed for an interdisciplinary audience, targeting professionals with a background in manufacturing, data analysis and other areas.
This is an introductory/intermediate Masters course (SCQF Level 11). It develops your skills and provides a detailed overview of the subject - some foundational knowledge or experience is required. Please see the entry requirements for further details. Masters-level courses are relatively intensive and require independent learning, critical thinking, analysis and reflection.
A UK 2:1 honours degree or its international equivalent in a numerate degree (Mathematics, Engineering, Chemistry, Physics, Computer Science).
If you do not meet the minimum academic requirement, you may still be considered if you have relevant professional qualifications or experience, preferably including at least one of the following:
- Basic knowledge of programming
- Experience working in the field of manufacturing
- Basic knowledge of data handing using Python
- Other experience working with data and interested in how they can be used in manufacturing
English Language Requirements
You must be comfortable studying and learning in English if it is not your first language.
On completion of this course, the student will be able to:
- Understand/implement computer models of common engineering information types.
- Understand the importance and be able to critically discuss the role of management information systems for design, engineering and manufacturing.
- Discuss and evaluate engineering data management issues across the extended enterprise.
- Demonstrate an appreciation of the complex relationship between information systems and organisation.
This is a 9-week course, comprising a total of 100 hours study (Lecture Hours 6, Supervised Practical/Workshop/Studio Hours 15, Revision Session Hours 3, Directed Learning and Independent Learning Hours 76)
This is a hybrid course – there will be on-campus teaching, but it is also possible to study online (lectures will be streamed and recorded, and the computer-based workshop can be done remotely).
Weekly lectures will take place on Fridays, 9am – 12 noon followed by a computer-based workshop. There will be a weekly drop-in clinic on Wednesdays, 7pm – 8pm.
Assessment is 100% coursework.
Course fees for 21/22 are £960 but funded places are available for people employed, unemployed or at risk of redundancy in Scotland who meet Scottish Funding Council eligibility criteria:
Through the Scottish Funding Council (SFC) Upskilling Fund and National Transition Training Fund, a number of fully-funded places are available at The University of Edinburgh on short, standalone courses from the Data Skills Workforce Development Training portfolio.
Funded places are available to those who meet Scottish Funding Council (SFC) fee waiver criteria. Note that course places are limited and priority will be given to those who meet the criteria for SFC-funded places.
Determining eligibility for a funded place for upskilling takes a number of things into account, including fee status, but also location of employer. SFC provide guidance specifically for upskilling courses:
“Courses/provision is open to all Scottish-domiciled/’home fee’ students, which is consistent with SFC’s policy for core funded student places. Students from the rest of the UK (rUK) are not normally considered eligible for SFC funding. If however a university is working with a Scottish/UK employer which has a physical presence or operating in Scotland, rUK employees of that employer would be eligible.”
Organisations like UKCISA, and the University, provide guidance on how to determine your fee status:
- Information on current fee status regulations for studying in Scotland is available here: https://ukcisa.org.uk/Information--Advice/Fees-and-Money/Scotland-fee-status-for-students-starting-from-August-2021.
- You can check your likely fee status here: https://www.ed.ac.uk/tuition-fees/fee-status/work-out
If your fee status is Scotland Fee Rate, RUK Fee Rate, or EU-EEA Pre/Settled Scotland Fee rate, you may be eligible for a funded upskilling place. To determine this we will look at your fee status, residency information and, where relevant, details of your employer, to confirm whether the employer is based in, or has a significant presence, in Scotland.
Funding eligibility will be assessed at the point of each application for each course; you may be asked to provide further information if you do not meet the general residence conditions. Please email us at email@example.com if you would like to discuss your funding eligibility before applying.
Please note that full-time students (including full-time PhD students) are not eligible for funding.
Funding eligibility criteria for the National Transitition Training Fund in the 2021/22 session is still to be confirmed.
How to Apply
Courses are now being updated for the 2021/22 academic year.
Each course will have a distinct start date which will be updated on the Schedule of Courses webpage and each Course page.
Application links will be available in each individual course webpage, which can be accessed from the Courses summary page. Details about entry requirements and any supporting documentation required can also be found on each page.
You can register your interest by joining our mailing list where courses do not have a live application form for this session.
If you have questions about the application process please contact:
You may find additional information in the FAQ section helpful as well:
You will receive a certificate of attendance after the final assessment date if you have submitted your coursework. If you pass the course, you will receive a certificate of completion once marks have been ratified by the Board of Examiners - this may be several months after the final assessment deadline.
Applications for February 2022 are now closed.
You can use credits achieved on this course towards postgraduate study on the Digital Design and Manufacture programme (MSc, PG Diploma or PG Certificate), subject to approval by the Programme Director. This programme provides students with a broad understanding of the theories and practices required to enable successful implementation of digital technologies in industrial applications.
You may also be able to use credits achieved on this course towards other University of Edinburgh postgraduate programmes, subject to the approval of the relevant Programme Director: