In today’s data-driven world, the field of statistics continues to evolve rapidly, offering diverse and impactful career opportunities across sectors including academia, government, industry, international development, and more. However, many early-career statisticians and students, including those in Africa or of African descent, seek skill development resources to sharpen their technical skills and apply their learning to practical settings that may not be available in their day-to-day environments.
This joint webinar, hosted by YSS and YASA, aims to bridge that gap by providing skill-based training on a topic in statistics and data science by experienced statistician(s). The theme of the skills-based training this year is on data visualisation. Although the focus is on young African statisticians, we welcome anyone to attend and embrace this journey.
The webinar seeks to:
- Provide hands-on training on a topic in statistics and data science
- Enhance participants’ technical skills and understanding of the topic
- Build participants’ confidence in using the technical skills in practical settings
- Foster a supportive learning environment to exchange knowledge and apply the skills
- Facilitate a cross-regional dialogue between young statisticians from Africa and the UK, fostering mutual learning and future collaboration.
Data visualisation can be a very efficient method of identifying patterns in data and communicating findings to broad audiences. Good data visualisation requires appreciation and careful consideration of the technical aspects of data presentation. But it also involves a creative element. Authorial choices are made about the “story” we want to tell, and design decisions are driven by the need to convey that story most effectively to our audience. Software systems use default settings for most graphical elements. However, each visualisation has its own story to tell, and so we must actively consider and choose settings for the visualisation we are building. In this talk, Nicola will showcase why you should visualise data, present some guidelines for making more effective charts, before discussing examples of good and not so good charts.