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It is the supreme art of the teacher to awaken joy in creative expression and knowledge
A. Einstein
@Mathematical Institute, University of Oxford (2019). Credit: Dr B. Liu
@Big Data Institute, University of Oxford (2019). Credit: MAP team
Current courses
Fundamentals of Data Science: Fall and Winter terms (2023-current), Zhejiang University Haining International campus, Internanational Master of Data Science programme.
Geospatial Modelling with R-INLA: a 4-module online interactive tutorial (2022-now), Zhejiang University - more info below.
Previous courses
Data visualization in R: a 32h course (2021/2022), Zhejiang University
Introduction to R. Lecture given to PhD students in Mathematics, Mathematical Institute, University of Oxford (2019).
R: kick-off. Regular lecture given to staff and students (2019), IT services, University of Oxford.
Introduction to Bayesian statistics. Lecture given to GMS PhD students (2019 and 2020), Wellcome Centre for Human Genomics, University of Oxford.
R: visualising your data. Regular lecture given to staff and students (2019 and 2020), IT services, University of Oxford.
Big Data Institute Spatial Analysis Training Day, September 5th, 2019.
Geostatistical modelling of Martian Dunes with R-INLA
About the course
In this course, learners will learn the fundamentals of programming in R, spatial data, and geostatistical modeling before engaging into R-INLA (https://www.r-inla.org/), a toolbox used to efficiently fit Bayesian geostatistical models. The course is developed using R Markdown and runs on a Shiny Server, providing a highly interactive, browser-based learning experience to faciliate the learning process. No installation of R or any other software required. Users can watch multimedia content, interact with formulas, run code blocks, view results, and complete quizzes directly within the tutorial.
The exercises use real Martian dune data, offering an intuitive and effective data analysis experience through live coding and immediate feedback. By the end of the course, learners will be able to apply and interpret R-INLA models using their own domain-specific data.
Designed to be beginner-friendly, the course minimises complex mathematical formulas to make R-INLA spatial modeling accessible and practical. It is ideal for students and professionals in statistics, spatial geography, medicine, biology, sociology, and anyone interested in learning R.
The course is online and interactive. It does not require any software installation and it is offered in both English and Chinese.
To get more information and/or register, you can visit: http://course.bayestat.com/rinla/WebsiteEn.html. Feel free to contact the course administrator (Dr. Liu) at BayeStat@outlook.com if you have any questions about the course.
Course modules
1. Introduction to R
Learning basic knowledge to program in R and getting familiarized with the dataset (dunes on the planet Mars).
2. Spatial data
Immersion into the world of spatial statistics, a branch of statistics that studies spatial data. Acquisition of fundamental knowledge on spatial data and introduction to key concepts in spatial modeling with a focus on geostatistical models.
3. Bayesian statistics
A gentle introduction (mathematic formulations kept to a minimum) to Bayesian statistics to get the necessary knowledge to build, interpret, and fine tune geostatistical models with R-INLA.
4. Bayesian Geostatistical Modeling with R-INLA
Learning how to code and interpret the results of Bayesian geostatistical models applied to Martian dunes.
Feedback from previous teaching sessions of R-INLA
Peking University
Course offered online and offline to 65 partipants across China @S3 Lab, Peking University (2020)
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