Are you ready to dive into the future of statistical work in drug development?Are large language models like ChatGPT the future of data analysis?How does the increasing trend towards transparency and open-source programming influence the pharmaceutical industry? In this episode, Benjamin Piske and I explore the dynamic landscape of trends shaping the statistical field. As …
Trends That Will Have an Increasing Impact on Our Work Read More »
As a statistician, you’re likely already familiar with SAS and the value it brings to data analysis. But in this ever-evolving world of statistics and data science, relying on your go-to language might not be enough to stay competitive anymore. The open-source language R is gaining traction within the healthcare industry as a powerful tool …
In any industry, staying competitive and relevant requires a constant focus on innovation and the ability to communicate effectively with customers. In the pharmaceutical industry, this is particularly true as companies race to develop and market new compounds that can improve patient outcomes. In this episode, I share insights into the importance of effective data …
Your compound through the eyes of the competition Read More »
Interpretable machine learning (IML) is rapidly gaining popularity in the data science community. It offers a new way to build and interpret models that are more transparent and understandable. In this episode, we have the privilege of interviewing Serg Masis. Serg authored the book “Interpretable Machine Learning with Python: Learn to build interpretable high-performance models …
For years, creating data visualizations has frustrated statisticians, data analysts and data scientists. Software – especially SAS – hasn’t been great and organisation have tried to take the pain out of the process by creating standard figures. But these standard graphics fail to communicate the message to the intended audience in the best way. Great …
Why is it impossible to have a great standard data visualisation? Read More »
Anyone can create a data visualization but not everyone can create a data visualization that is effectively communicating the data – and this is what we should aim for. Done wrong they leave the audience confused. In this episode, I’ve gathered up six problems around creating data visualisations, so you can improve the quality of …