Service Desk
Training portfolio
This is a general overview of the core training that CSC and our partners offer throughout the year. Most courses are repeated once or twice a year, even though specific dates might not have been scheduled yet.
Scheduled trainings can be filtered in the Training calendar, where you will also find courses not included in our main portfolio offering. Past training events are found in the Training archive.
Trainings listed by category
- AI and Data Analytics
- Bioinformatics and Life sciences
- Chemistry
- Cloud Computing
- Data Management
- Fundamentals
- Geoinformatics
- High Performance Computing
- LUMI
- Physics
- Programming
- Quantum Computing
- Sensitive Data
AI and Data Analytics
See scheduled trainings and past trainings
| Training | Training overview | Schedule |
|---|---|---|
| Data analysis with R | Learning goals: Learn the basics of R for data wrangling, plotting and statistics. Prerequisites: None. Materials: GitHub | September–October |
| High performance R | Learning goals: Use R efficiently to make the most of computing resources in local and supercomputing environments. Prerequisites: Basics of the R programming language. | October–November |
| Practical deep learning | Learning goals: Participants learn skills and knowledge needed to begin applying deep learning for different tasks and utilizing the GPU resources available at CSC for training and deploying their own neural networks. Prerequisites: Python, Linux, ML fundamentals. Materials: GitHub | April, October–November |
Bioinformatics and Life sciences
See scheduled trainings and past trainings
| Training | Training overview | Schedule |
|---|---|---|
| MOOC: Spatially resolved transcriptomics with Chipster | Learning goals: Use Chipster to analyze spatially resolved transcriptomics data. Prerequisites: Single-cell RNAseq data analysis with Chipster or similar knowledge. Materials: Links to self-study materials here | Self-study |
| MOOC: Single-cell RNA-seq data analysis using Chipster | Learning goals: Use Chipster to analyze single-cell RNA-seq data. Prerequisites: Basics of single-cell RNA sequencing. Materials: Links to self-study materials here | Self-study |
| Analysis of bulk RNA-seq data using Chipster | Learning goals: Introduces RNA-seq data analysis methods, tools and file formats. Prerequisites: Basics of bulk RNA-sequencing. The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or R is required. Materials: Chipster website | April |
| Containers and workflows in HPC environment | Learning goals: Fundamentals of containers, focusing on their deployment in an HPC environment. Prerequisites: Linux, editors, HPC. Materials: Course website | November |
| Microbial community / environmental DNA analysis with Chipster | Learning goals: Workflow from quality control and filtering to quantification and statistical analysis using Mothur and Phyloseq tools integrated in the user-friendly Chipster software. Prerequisites: Basic understanding of amplicon sequencing. No knowledge of Unix or R is required, because in the exercises we use analysis tools such as Mothur, DADA2 and Phyloseq integrated in the user-friendly Chipster software. Materials: Chipster website | March–April |
| Single-cell RNA-seq data analysis with Chipster | Learning goals: Introduces single cell RNA-seq data analysis methods, tools and file formats. Prerequisites: Basics of single-cell RNA sequencing. The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or R is required. Materials: Chipster website | March–April |
| Single-cell RNA-seq data analysis with R | Learning goals: Introduces single-cell RNA-seq (scRNA-seq) data analysis concepts and R packages. Prerequisites: Some experience using R. Materials: GitLab | March–April |
| Spatial transcriptomics (Visium) data analysis with Chipster | Learning goals: Introduces the analysis of spatially resolved transcriptomics (Visium) data. The course covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, cell type identification and detection of spatially variable genes. Prerequisites: Single-cell RNAseq data analysis with Chipster or similar knowledge. The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or R is required. Materials: Chipster website | April–May |
Chemistry
See scheduled trainings and past trainings
| Training | Training overview | Schedule |
|---|---|---|
| CSC Spring School on Computational Chemistry | Learning goals: Gain an overview of the two main branches of computational chemistry, related HPC software packages and special topics such as enhanced sampling and machine learning methods. Prerequisites: Linux, Python, Jupyter Notebooks. Materials: Zenodo | April |
| Schrödinger Maestro workshop | Learning goals: Introduction to the Schrödinger Maestro software suite for life science and materials science simulations. Varying content covering, for example, molecular dynamics simulations with Desmond and docking with Glide. Prerequisites: None | October–December |
Cloud Computing
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| Training | Training overview | Schedule |
|---|---|---|
| Pouta course | Learning goals: The course covers the basics of the platforms and how to become a user. Prerequisites: None. Materials: Course slides | April–May |
| Rahti course | Learning goals: Introduction to containers, application templates, web interface, storage, high level Kubernetes architecture and command-line tool. Prerequisites: None. Materials: Course slides | April–May |
Data Management
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| Training | Training overview | Schedule |
|---|---|---|
| CSC’s self-study “Research data management” course / CSC:n itseopiskeltava Tutkimusdatanhallinnan kurssi | Learning goals: The course covers the basics of data management and gathers resources and tools available for different stages. Prerequisites: None. Materials: Links to self-study materials in English & Finnish | Self-study |
| A changing data management theme, for example: The benefits and challenges of opening up data – A researcher’s perspective (spring & autumn 2025) | Learning goals: The aim of the webinar is to help to understand the process of opening data and possibly lower the threshold for opening data. Prerequisites: None. Materials: Eduuni wiki | April–May September–October |
| Using the Allas Storage Service / Allas-säilytyspalvelun käyttö | Learning goals: Usage of Allas storage service. Prerequisites: None. Materials: Eduuni wiki | March–April (Finnish) May–June (English) |
| Fairdata Services for Research Data: Utilize, Manage, Publish! / Fairdata-palvelut tutkimusdatalle: Hyödynnä, hallitse, julkaise! | Learning goals: Gain a comprehensive overview of Fairdata services, their benefits, and how to get started with them. Prerequisites: None. Materials: Eduuni wiki | March–April (Finnish) May–June (English) |
Fundamentals
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| Training | Training overview | Schedule |
|---|---|---|
| CSC Research Support Coffee / Viikoittaiset kahvitaukotapaamiset tutkimustukemme kanssa | Learning goals: Ask us anything! Prerequisites: None. Materials: HedgeDoc | Every Wednesday at 14:00 Finnish time |
| CodeRefinery | Learning goals: In this course, you will become familiar with tools and best practices for scientific software development. Prerequisites: None, but it helps to know something about programming in any language. Materials: CodeRefinery lessons | Every spring & autumn |
| Linux-1 – Linux crash course | Learning goals: Covers basic terminology and history of Linux, filesystem hierarchy, working with CLI, configuring users and working with files and processes. Prerequisites: None. Materials: GitHub | October |
| Linux-2 – Advanced shell scripting | Learning goals: Using scripting languages (mainly bash and Python) and their integration with Linux. Pipes, regex, a bit of sed and awk. Prerequisites: Linux-1 or equivalent level of knowledge of Linux. Materials: GitHub | September–December |
Geoinformatics
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| Training | Training overview | Schedule |
|---|---|---|
| Geocomputing on the supercomputer | Learning goals: Learn the basics of geocomputing on a supercomputer through a combination of lectures and hands-on activities. Prerequisites: Understanding of geoinformatics, Python, R or use of command-line tools. Materials: Course website | October |
| Spatial data analysis with Python | Learning goals: This course teaches you how to do different GIS-related tasks in Python programming language. Prerequisites: Basics of geoinformatics, basic use of Python. No earlier experience with Python GIS packages is needed. Materials: Course website | Self-study |
| Spatial data analysis with R | Learning goals: The aim of the course is to familiarize participants with spatial analysis with R. Prerequisites: Basics of geoinformatics and geostatistics. Basic use of R, no earlier experience with R spatial packages is needed. Materials: Course website | Self-study |
High Performance Computing
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| Training | Training overview | Schedule |
|---|---|---|
| Elements of Supercomputing | Learning goals: Theoretical knowledge needed to explain the basic principles of a supercomputer and high-performance computing. Prerequisites: None Materials: Course website | Self-study |
| CSC Summer School in High-Performance Computing | Learning goals: Build HPC expertise up to an intermediate/semi-advanced level during the school. Prerequisites: The school is aimed for graduate students working in various fields of science. Materials: GitHub and Blog | June–July |
| CSC Computing Environment, Part 1: Basics | Learning goals: Learn the basics of using the CSC HPC environment efficiently. Prerequisites: Basic Unix skills, CSC credentials & project. Materials: Course website | March, October |
| CSC Computing Environment, Part 2: Next steps | Learning goals: Get familiar with more advanced topics related to using the CSC HPC environment efficiently. Prerequisites: Basic Unix skills, CSC credentials & project, “CSC Computing Environment, Part 1: Basics” or similar knowledge. Materials: Course website | April, November |
| GPU Programming with CUDA/HIP | Learning goals: Learn programming GPUs using CUDA/HIP application programming interfaces. Prerequisites: Basic experience in programming with C or C++. Materials: GitHub | Spring |
| Portable GPU Programming | Learning goals: Learn programming GPUs using portable, hardware-agnostic frameworks. Prerequisites: Basic experience in programming with C++. Materials: GitHub (previous course under different name) | Autumn |
LUMI
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Physics
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Programming
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Quantum Computing
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| Training | Training overview | Schedule |
|---|---|---|
| Introduction to quantum computing and FiQCI | Learning goals: Two-day introductory course on quantum computers and the basic quantum algorithms that control them. Prerequisites: Basic programming skills and familiarity with the Jupyter Notebook environment is an asset. | August–September |
| Quantum Machine Learning | Learning goals: A two-day introductory course on quantum machine learning. Prerequisites: Basics of Qiskit and quantum computing. | November |
Sensitive Data
See scheduled trainings and past trainings
| Training | Training overview | Schedule |
|---|---|---|
| On demand Sensitive Data (SD) Services training | Learning goals: This course is available on demand and will be tailored to the needs of your research project or organization. Whether you prefer using a web user interface or programmatic access, we customize the content based on your experience level. Prerequisites: None. Materials: See how to request the course | On demand |