Do you want to help someone improve their visualization skills?
Or do you want to train others on any other statistics skills?
Training is a very important topic for us statisticians. We train people all the time. It can be about statistics or non-statistics.
Today, I’ll be sharing some of my insights, tips, and tricks on how to do that in an effective way.
- Set expectations
- What do they think they will get in the training?
- What are their goals?
- Start with a case story
- Show the value of visualization
- Speak about the difference between explanatory and exploratory data visualization
- Where do they mostly work with?
- Create a connection with the audience
4. Speak through an example
- Ask them to sketch the data by using pen and paper
- Create an emotional trigger
- Repetition improves quality
- Provide self-evaluation
- Supportive Feedback
- Eliminating clutter
5. Review the learnings
- Reflecting on what they have learned
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This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
