Available courses

Principles of Machine Learning for Official Statistics and SDGs

Principles of Machine Learning for Official Statistics and SDGs

 Registration to the self-paced course is suspended!

A new facilitated session of this course will start on 27 November, we encourage you to enroll (more soon)  

The course aims at providing an overview of the current methods and applications of Machine Learning, through theoretical concepts, pedagogical case studies and interactive resources. The course is not based on, nor does it require, a particular software. However reproducible examples are provided using the R/RStudio environment and Python.

See the flyer and the concept note


Increasing User Engagement Around Data and Statistics

Increasing User Engagement Around Data and Statistics

Facilitated learning

The statistics and data produced by National Statistical Offices (NSOs) and other National Statistical System (NSS) agencies do not exist in a vacuum. We can produce the most 'perfect' data in the world, but they are of little value if they are not used. It's therefore essential that we as producers of official statistics engage with our users in order to both understand and respond to their needs. 

Learning objectives: By the end of the course, participants will be able to:

  1. Understand what user engagement is and why it is important for statistics
  2. Identify who their user and potential users are
  3. Apply different tools/methods for engaging with users
  4. Better respond to user needs through effective data storytelling. 

Principles of Data Visualization for Official Statistics and SDG Indicators

Principles of Data Visualization for Official Statistics and SDG Indicators

This course introduces data visualization as a tool to produce high-quality graphics for monitoring official statistics and the Sustainable Development Goals (SDGs) indicators.

The course is not based, nor does it focus, on any software. However, some popular software will be introduced and used in the course (Excel, QGIS, R/Rstudio, R Shiny, Google Sheets) as well as references to online tools. 

See the flyer and the concept note