Data Science for Manufacturing
Gain a practical grounding in the technologies of data science and their potential uses in manufacturing enterprises with our hybrid, credit bearing course. The course involves weekly lectures and hands-on computer workshops. The course introduces the python programming language and teaches you to apply some of its tools for manipulating and analysing data common to manufacturing businesses.
This online course is ideal for learners who are familiar with the basic processes commonly used to organise and undertake engineering manufacture and who are looking to expand and improve that knowledge.
Participants will learn about the types of data that can be found in manufacturing, how manufacturing data can be accessed, aggregated and analysed, and how the data can be used to optimize processes. The course also covers exploratory data analysis, the importance of data science in manufacturing, tools for data analysis, tools and techniques for data visualisation and evaluation, and introduces you to the ramifications of data collection and use in a manufacturing setting.
Participants will gain knowledge of the types of data which can be found in manufacturing, how manufacturing data can be accessed, aggregated and analysed, and how the data can be used to optimise processes.
Topics also include:
- Python Coding and Notebook Programming Environments,
- Data Carpentry,
- Data Visualization,
- Relational and Graph Data Structures,
- Forms of manufacturing data (e.g. Supply Chain (Supply Chain, ERP, PLM, CAD/CAM),
- Machine learning/Data analysis.
Participants will also develop an understanding of data formats, their wrangling and management including CSV and relational databases (SQL), CAD formats and materials. They will develop skills in the analysis and visualisation of a range of data using descriptive statistics and exploratory data analysis. As well as learning how to critically evaluate data use and practices.
The course is led by Prof Andrew Sherlock, Director of Data-Driven Manufacturing at the National Manufacturing Institute Scotland (NMIS) and Professor of Practice at the University of Strathclyde, Prof John Corney, Professor of Digital Manufacturing in the School of Engineering at The University of Edinburgh and Dr Danai Korre, Research Associate in Augmented and Virtual Reality in the School of Engineering at The University of Edinburgh.
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.
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
You must be comfortable studying and learning in English if it is not your first language.
- 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 10-week course, comprising a total of 100 hours study.
This is a hybrid course – there will be on-campus teaching in Edinburgh, but it is also possible to study online (lectures will be streamed and recorded, and the computer-based workshop can be done remotely). Lectures will start on the morning of Friday 22nd September 2023 with an optional, one day, Python introduction course a week before, on Friday 15th September 2023, and a 1 hour information session, date to be confirmed.
Weekly lectures will take place on Fridays, 9am – 10am, followed by a computer-based workshop, 10am - 12noon. There will be a weekly drop-in clinic on Wednesdays, 7pm – 8pm.
Assessment is 100% coursework.
Course fees for 23/24 are £1,065 but funded places are available for people employed or unemployed in Scotland (residency requirements apply).
Funding
Through the Scottish Funding Council (SFC) Upskilling Fund, a limited number of fully-funded places are available on Data Upskilling Short Courses at The University of Edinburgh.
Eligibility
Funded places are available to those who meet SFC fee waiver criteria:
“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.”
If you are from outside Scotland, you need to have settled status in the UK and meet other residency criteria:
- be ordinarily resident in the United Kingdom, the Channel Islands or the Isle of Man for the three years immediately before course start date, and
- have ‘settled status’ in the UK (as set out in the Immigration Act 1971) at the course start date, and
- be ordinarily resident in Scotland at the course start date.
You can find out more about residency criteria on the SAAS website or in this summary.
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. You can check your likely fee status here: https://www.ed.ac.uk/tuition-fees/fee-status/work-out Please email us at upskilling@ed.ac.uk 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.
You will receive a certificate of completion after the final assessment date if you have submitted your coursework.
Applications for September 2023 have now closed.
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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:
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