Scientific Data Handling and Programming

For Advanced Learners

Scientific Data Handling and Programming


The ability to form research questions and test our hypothesis with data is a keystone skill in science. This course will equip students to understand data collection and analysis in the context of research. The course will be a practical guide to completing data collection and analysis for research with a “hands-on” approach.

Fundamental skills in R, Python, GIS, and other relevant computer programming languages will be discussed as tools to extract knowledge from the data. Statistical theory will be discussed in the context of problems students will encounter throughout the course to assure a comprehensive understanding without being overwhelmed. Students will learn the research process from beginning to end which will culminate in a short research project being completed to compile the skills learned throughout.





This course is designed for under- and post-graduate students, as well as scientific and technical professionals (NFQ levels 6) and delivered in both Online and Virtual Classroom environments.

In addition to classes, course material/handouts will be accessible to students for further study either as hard copy (charge may apply) and/or online (Virtuline Hub).

Classes will be a mix of lectures, practical and supervised research. The course will be assessed through inclass exercises and through the completion of a small research project where the skills acquired in class will be practiced.

MODULES (Part 2)



Introduction to research methodology
● Understanding experimental design.
● Understanding the data required to answer questions.
● Learning data collection methods.

Introduction to data handling
● Building a database.
● Reading and writing data.
● Understanding dataset curation.

Introduction to programming
● Introduction to R stats for science.
● Introduction to Python for science.
● Introduction to GIS for science.

● Research mini-project + assignments
● Online class exercises.
● Research mini-project on a topic chosen.



● Understanding research.
● Hypothesis formation.
● Basic programming skills.
● Data science skills and
● Scientific writing.


● Academic research e.g. Research MSc and PhD.
● Data science jobs across industries including academic and research institutes.
● Government research jobs.
● Consultancy jobs.




● Opportunity to practice data analysis and programming in areas of special interest.
● Improve skills while enjoying free time with leisure activities.
● Access to material posted on our school online platform.
● Support and feedback from qualified teachers.


Key Facts

● Course duration: 10 weeks (20 hours taught, 10 hours assignments; 60 hours self-directed learning, and 30 hours final project).

● Entry level: Open to all/Understanding level.

● Lesson duration: : 2 hours per lecture + 1 hour assignment per week.

● Size of the class: 25 in a Virtual Classroom .

● Course timetable: Tuesday Evening and Saturday Morning.

●Course structure: One day per week and one day per weekend.

Course Fees

Course Name Course Fee
Scientific Data Handling and Programming For Advanced Learners