Data Science Using R (2026)
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Welcome to Data Science using R
Description: In todays data-driven world, the ability to handle ...
Welcome to Data Science using R
Description: In todays data-driven world, the ability to handle quantitative data systematically and reproducibly using reliable software is essential. This course, Data Science with R, is designed to introduce you to R, a powerful and free programming environment that has been a cornerstone of data analysis and statistical methodology for over two decades. R a versatile tool and is widely used in academia, industry, and government for a variety of data analysis tasks. This includes, for example, biostatistics, bioinformatics, analysis of data from life sciences, medicine, biology, engineering, physics and social sciences.
Data Science with R will equip you with the skills to tackle a variety of data challenges through a blend of practical programming and theoretical knowledge.
The curriculum encompasses:
- Introduction to R: Discover R as a premier tool for statistical programming and data analysis.
- Efficient Data Management: Master techniques for managing and manipulating data efficiently.
- High-Level Graphics: Create stunning, informative visualizations with R's advanced graphical capabilities.
- Statistical Modeling: Dive into theoretical and practical aspects of statistical modeling.
- Big Data Analytics: Learn how to extract insights from large datasets.
- Reproducible Research: Implement reproducible research practices to ensure your work can be trusted and verified.
- R Programming: Develop robust programming skills in R to solve complex data problems.
- Application areas: Examples include biostatistics and analyzing data from life sciences, medicine, social sciences, spatial data, physics, engineering, and more.
To help you asses if your statistical background knowledge is adequate you should feel comfortable with the material presented in the first five videos in. If you feel fairly comfortable with the material in the first five videos of this playlist (it takes about an hour).
Learning objectives: Upon completing the course, participants will:
- Master R Programming: Confidently solve programming tasks using R.
- Data Management and Visualization: Effectively manage, visualize, and analyze data using R's powerful tools.
- Model Fitting: Fit and interpret statistical models to extract meaningful insights from data.
- Independent Learning: Acquire sufficient knowledge to continue exploring and advancing their R programming skills independently.
- Competitive Edge: Be at the forefront of modern data science methods, enhancing your competitiveness in the field.
Teaching methods: The course employs a dynamic blend of instructional approaches, including: Instructive Videos: Engaging video tutorials to introduce and explain key concepts. Computer Practicals: Hands-on sessions to apply learning in real-time, practical scenarios. Lectures: Comprehensive lectures to provide theoretical foundations and contextual understanding.
Criteria for assessment: Active participation in the practicals + approval of major exercise (to be handed in after the course).
Key literature: R for data science by Wickham, Cetinkaya-Rundel and Grolemund
Organizer: Ege Rubak
Lecturers: Ege Rubak and Søren Højsgaard
ECTS: 4.0
Time: 04, 05, 11, 12, 18 and 19 March 2026
Place: Aalborg University
City: Aalborg
Number of seats: 50
Deadline: 11 February 2026
Important information concerning PhD courses:
There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.
For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.
To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.
We cannot ensure any seats before the deadline for enrollment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.
For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.