Certificate in Process Data Analytics
Level 8 About NFQ
David GouldingEmail Phone
Department of Mathematics,
Cork Institute of Technology,
Head of Department
CIT's Certificate in Process Data Analytics has been designed and developed in collaboration with industrial experts in the fields of Data Science, (Bio)pharma, Medical Devices, and other Manufacturing industries. The programme aims to furnish students with the necessary skill sets to enter the world of data analytics through building strong foundations in the core competencies of Statistics, Programming and Data Analytics. This programme introduces students to topics including regression, visualisation, data mining and machine learning while also providing the learner with the required technical skills in software packages and industry standard programming languages including R for example.
Applicants must hold a Level 8 degree and must be highly motivated and capable of independent learning. Preference will be given to applicants with a background in cognate and analytical disciplines, who would benefit from an opportunity to rapidly and successfully convert their qualifications to industry-relevant skills. All candidates with a Level 8 qualification or equivalent will be considered.
Candidates with a Level 7 qualification and significant relevant experiential learning may be eligible through our recognition of prior learning (RPL) processes. CIT has an extremely well-established and supported RPL process (please see www.cit.ie/rpl for further details).
Competitiveness is critical as manufacturing sites within networks globally compete to be selected to manufacture produces. With advancements in manufacturing process technologies and the drive towards Industry 4.0, companies increasingly seek to make data-driven decisions about both their operations and supply chains to achieve this competitiveness.
Process data analytics refers to a combination of tools and techniques that are used to make inferences and process decisions based on measured system data. The field of data analytics has become progressively important due to the huge increases in the amount of data being collected, reductions in the cost of computer hardware, advances in data analytics algorithms, and the increased availability of powerful software tools.
This Certificate in Process Data Analytics has been designed, with significant industrial engagement, to provide learners with an opportunity to rapidly upskill in the ever-expanding field of data analytics. The programme will provide graduates with a theoretical underpinning of process data analytics, but more importantly, provide the practical skills required to meet the demands of the current and future data-driven industry. This course will enable learners to apply the transferable skills developed as part of their original degree to a specific expertise within data analytics.
This part-time blended learning programme will run over one academic year, for three evenings per week. The bulk of the course will be delivered online, providing a flexible learning environment for applicants.
During the programme, students will undertake the following modules (the module DATA8011 is a 10 credit module while the others are 5 credits):
DATA 8010 - Introduction to Data Analytics
This module examines the use of statistics and data science for various types of data from industrial processes. Learners will understand the importance of a data system in an individual site of a multinational network running manufacturing operations. Best practice and high-tech solutions to data management and modelling will be explored and students will learn how to communicate statistical findings to a wide audience.
DATA8009 - Process Data Analytics with R
Here the learner is exposed to the statistical programming language R for analysis of process data such as process monitoring as well as exploring data management. Common workflows including cleaning data will be explored and appropriate visualisations generated. Best practice in version control and reproducible documentation will also be examined.
STAT8011 - Regression Analysis
Regression analysis is the most widely used tool in statistical modelling. Students will examine regression in the context of industry based data linked to experimental design. Students will also learn how to conduct ANOVA and logistic regression which are widely used in industry.
DATA8011 - Data Mining and Visualisation
In this module, the learner will investigate a variety of advanced visualisation concepts and tools for analysing multi-dimensional data, large datasets and complicated process datasets. The creation and use of dashboards will be examined. The learner will also examine data mining - the discovery of patterns and knowledge within large amounts of data. The learner will study a variety of data mining algorithms and models to solve various real-world problems.
DATA8012 - Data Analytics Case Study
This module develops within the learner the knowledge, skills, and competences required to scope and implement a data analytics project. The module requires the learner to develop, implement and critically assess a detailed methodology to address a defined analytics problem within a prescribed time frame. The learner is expected to be self-motivated whilst working under direction of a project supervisor and to communicate the process and outcomes of their work in a style and manner appropriate for professional practitioners in the discipline.