9 Learnings from Over 100 Episodes over 2 years
And today, we are looking back and discuss the last 2 year’s learnings. We discuss the 9 learnings we had and how they apply to you as a listener as well.
And today, we are looking back and discuss the last 2 year’s learnings. We discuss the 9 learnings we had and how they apply to you as a listener as well.
In episode 3 of this mini-series, I discuss the impact of simplified data visualization and how to achieve it. Specifically, I address these factors that affect your visualization.
We all know how to specify a table and our processes support this. However, designing an awesome data visualization does not work this way.
In today’s episode, I want to express what’s on my mind and on my heart about this topic. It’s a bit of an unusual episode, as I’m very passionate about the topic. You can agree or disagree with my thinking. Please let me know what you think about it.
The COVID-19 outbreak forces many of us to work from home. But even before this, working from home became a growing trend, most especially at the moment.
In today’s episode, Stephen and I discuss the main points of his presentation and the take-away learnings for every statistician – even if you don’t work in a large pharma company.
There are lots of buzzwords floating around and especially with the FDA having a bigger focus on RWE it’s top of mind for many stakeholders. Many teams in the different pharma organizations concentrate on this type of evidence.
In today’s episode, Liz Cole and I will dive deep into a topic called content marketing. It’s kind of meta, since this podcast itself originates on these ideas. Does this sound frightening or disturbing? It shouldn’t.
In today’s episode, we will talk about the data ops manifesto which addresses such problems. There are actually a couple of such manifestos out there such as the agile manifesto or the dev ops manifesto. These sound similar to Demings 14 principles outlined in his “out of the crisis” publication.
In today’s episode, Benjamin and I discuss some backgrounds for baseline testing in randomized studies.