The Springboard HCI funded Graduate Diploma in Agri-Analytics is a a cross-disciplinary programme developed collaboratively by three academic departments in DkIT. It will cover advanced skills in database management, data collection and analysis in addition to an in-depth analysis of the application of information technologies in Agriculture.

According to the Teagasc Education Vision study (2018), investment in new and existing technologies will play a decisive role in enabling the Agriculture sector to sustainably intensify production and to grow output, exports and jobs, while respecting the environment.

'Harnessing this transformation will not only enable ambitious increases in the export of world-class agricultural produce, but will also drive the completion of a dynamic circular bioeconomy, creating new jobs and new opportunities. It will help to increase profitability throughout agri-food value chains. It will drive exports of smart knowledge-based data-driven services, developed by Irish service providers, to markets in Europe, and across the globe'.

There is a general consensus (see below) that modern agriculture will require farmers to develop more advanced skills sets to enable them to meet the fundamental changes occurring at all levels. One of the identified skills sets is digital technology. The enhancement of skills required for 'Precision Agriculture' will be needed at all levels, on the farm, in the Agri-Food sector, in advisory services and in the growing 'agri-tech' sector.

This programme has been developed in response to this identified skills gap. It is a cross-disciplinary programme developed collaboratively by three academic departments in DkIT. It will cover advanced skills in database management, data collection and analysis in addition to an in-depth analysis of the application of information technologies in Agriculture. It will be delivered on a full time basis over the course of one academic year.

There will be 12 hours of face to face contact per week and these will be scheduled over two working days to facilitate learners already working in the sector or farming on part-time basis. Learners will participate in a research project in collaboration with Industry.

Please note: This programme is funded for eligible applicants via Springboard+ 2020 (as part of the Human Capital Initiative (Pillar 1).

Subjects taught

Students on the course will study the below modules:

Research Project: Research design, ethical and regulatory frameworks, IP, Collaborative industry Project.

Database Management: Data sources/prep/conditioning, data integration, SQL server functions, Cloud databases

Data Analytics: Statistical methods, data integrity/validation, computational methods (R), intro to machine learning

Bioinformatics in Agriculture (Elective): Applications in agricuture, workstations and tools, rata data analytics, high-throughput sequencing

Communications and Location Technologies: GPS, GIS, other geographical data, satellite imagery, modelling

Applied Technologies in Agriculture: ICT in crop and livestock management, precision animal management, wearable technologies

Embedded Systems and Sensors: Sensors (environmental, motion), yield monitoring, data logging, remote sensing

Automated Systems in Agri-Food (Elective): Robotics in waste management, agri-food production and intensive farm practices

Data Communications and Protocols: Data capture and processing, standards for technological communications, system power sources

The modules on the programme are highly applied and focus on the student on identifying and maximising transferable and industry specific skills by linking their learning to workplace practices and activities.

More specifically the students will carry out a research project in collaboration with the Agri-Tech Industry or an external agency managing agriculture data such as Teagasc.

The project will be allocated 20 ECTS credits (one third of the total credits for the programme) and will facilitate the application of learning from other modules on the programme as well as providing an opportunity for learners to develop a range of transversal skills. The project will comprise an advanced analytical technique (e.g. Monte Carlo Modelling, Bayesian Inference, Machine learning) or a working robotics prototype.

Entry requirements

Applicants must have an honours degree in Agriculture or a related area with an award grade of 2.2. or higher. Honours degree graduates from Engineering, Science and Computing disciplines will be considered where the applicant has an agricultural background and/or knowledge. Such candidates may be selected by interview.

In such cases, the DkIT Recognition of Prior Learning (RPL) policy process will be applied. DkIT recognizes that knowledge, skills and competencies can be acquired from a range of learning experiences, including formal and informal. Through its RPL policy, the Institute commits to giving appropriate recognition to all relevant learning, irrespective of mode or place of learning.The focus of the RPL process is on the outcomes rather than the process of learning. RPL candidates will need to demonstrate the appropriate academic level of their learning and, where relevant, produce evidence of practical skills.

Application dates

Closing Date: 20th January 2020

Assessment Info

Some of the modules combine in-class examinations in the form of multiple choice questions with a practical element – sign language/social media online assessment. The rest of the course is evaluated by continuous assessment through a combination of essays, reflective logs, a journal of learning, case study, oral presentation, and course design


1 year full-time


€2400 per academic year

Students who are registered on this programme are eligible to apply for the Financial Aid Fund for Part Time Students. Eligibility criteria applies. For more information see

Enrolment dates

Next Intake: January 2020 UCC

More details
  • Qualification letters


  • Qualifications

    Postgraduate Diploma (Level 9 NFQ)

  • Attendance type

    Full time,Daytime

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    Course provider