Data Science using R (2027)

Welcome to Data Science using R

Description:

In today’s 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.

Prerequisites:

A working knowledge of elementary statistical methods is required. This includes topics such as: summarizing data, estimation of e.g. mean and variance, hypothesis testing, confidence intervals, linear regression models, and analysis of variance. Such topics are covered in many courses, including the “Applied Statistics” course offered at Aalborg University.

Learning objectives:

Upon completing the course, participants will: 

(1) Master R Programming: Confidently solve programming tasks using R. 

(2) Data Management and Visualization: Effectively manage, visualize, and analyze data using R’s powerful tools. 

(3) Model Fitting: Fit and interpret statistical models to extract meaningful insights from data. 

(4) Independent Learning: Acquire sufficient knowledge to continue exploring and advancing their R programming skills independently.

Teaching methods:

Lectures, interactive programming sessions, exercises and videos.

Key literature:

Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. "R for Data Science" (2e)

Organizer: Ege Rubak

Lecturers: Søren Højsgaard, Ege Rubak

ECTS: 4.0

Date:

3, 4, 10, 10 17,  and 18 March 2027

Place: Aalborg University

City: Aalborg

Maximal number of participants: 40

Open for enrolment: 3 November 2026

Deadline for enrolment: 10 Feburary 2027

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 prioritized 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 enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

You may find more information in our FAQ: https://phd.moodle.aau.dk/local/page/faq

For inquiries not described in the FAQ, please contact the PhD administration at phdcourses@adm.aau.dk. When contacting us please state the course title and course period. Thank you.