The Effective Data Scientist

How can you get better, more reliable results from AI? In this episode, Paolo and Aziza explore why effective prompting is about much more than asking good questions. We discuss how structured prompting frameworks such as ROSES, CRISP, CARE, TRACE, and APE can help data scientists provide clearer instructions, reduce ambiguity, and improve the quality of AI-generated outputs. We also share practic...

The Effective Data Scientist Podcast is back—and this time, it’s bigger, broader, and more impactful than ever.

In this podcast episode, we discuss an important part of leadership – proactive behaviour. Or simply put—drive! As a statistician, your day-to-day approach to this topic will build up to long-term success. We discuss various aspects of the drive including:

In this episode, we’ll cover an amazing story by one of the best programmers and mentors I ever worked with - Shafi Chowdhury (www.shaficonsultancy.com). We’ll explore how it changed from being a freelance programmer only to building his company on the side. He had a great vision in mind, that drove him forward. You’ll also hear about his approach to teaching and mentoring – or in general helping...

000000E2 000000E0 00002DDD 00002879 000099FA 0000A568 00007FBA 00006CBA 00004C39 0000A224

0000016B 0000015F 000045CA 00003CF4 0013891F 000E045E 00007FBA 00006E82 00004C39 001366D6

Do you have a lot of email ping-pong, where emails go back and forth many times – too many times? Are you aware about the brand of you, that you communicate with your email style? Is email your default communication tool? Then this episode is for you. We have researched various articles on good email writing copies and distilled the best for you in this episode. By listening to this episode, you w...

Everybody uses email multiple times during the day and email represents a key communication channel. Managing the flood of emails effectively will reduce your stress. In this episode, we discuss various approaches that we learned over the years from different productivity experts. We discuss how we implement them in our daily routines.

In this episode, we share out ideas and experiences, which mindset sets up statisticians for success. We cover topics around:

In this episode, we share our ideas and experiences, which mindset sets up statisticians for success. Leading people Convincing business partners Delivering value and selling it–and what does selling mean Thinking outside the status quo to improve things in the long run Always learning about the business and the people in the business Learning about statistical innovation Doing things more effecti...

Communicating data is so important! Quarto is a fantastic tool for writing reproducible reports using literate programming. Literate programming allows us to incorporate documentation and code in the same program. The data science community has embraced this idea by adopting Rmarkdown and Jupyter Notebooks. Using Quarto efficiently, you can create parametrized reports, write scientific publication...

In this episode, Paolo interviewed Thomas on why sharing your code with R packages for some data science projects is essential. When is it important? Where to start? What are the main steps and best practices? This skill could be a game-changer in your career as a data scientist, and nowadays, it’s much easier due to the introduction of new tools.

We have outstanding news for you! Thomas Neitmann joins The Effective Data Scientist as a co-host of the show! Thomas is an Associate Director of Data Science at Denali Therapeutics. He contributed significantly to the adoption of the R software in the pharmaceutical industry - especially during his time at Roche.

In this episode, we move from the logistic regression model to the proportional odds model, with emphasis on interpretation and the checking of assumptions (visually and analytically). We also speak about the opportunities and challenges of dealing with the dichotomization of ordinal or continuous variables.

Logistic regression is a beautiful tool for modeling a binary dependent variable, although many more complex extensions exist. In the show, we will speak about the generalized linear model family, logit and probit functions, interpretations, and practicalities.

Creating reproducible research is crucial for data scientists as it ensures transparency, understanding, and accuracy in the research process. Not only does it help others understand your work, but it also allows for the reproduction and verification of your results in the future.

Ok – not everything, but in this episode you will get all the tips to make sure, you avoid the most common mistakes and that your code looks professional. Shafi Chowdhury is an expert programmer who has developed a style guide, which his clients apply broadly. He regularly gives trainings on SAS programming and build his own company based on these skills. He walks us through the different points a...

An awesome guided tour in the world of interpretable machine learning with Serg Masis. Serg Masis is data scientist at Syngenta, a leading science-based agtech company. Serg wrote an amazing book on interpretable machine learning [link]. All the code used for the examples illustrated in the book is freely available [link]. Serg drives us through the concept of interpretability and why it is better...

In this episode, we continue to dive into the linear regression model. What are the real-world applications? When our model fit is enough? What are the pros and cons of increasing complexity in our model? We discuss also the basic principles of covariates transformation (i.e. the logarithm) and how this has played a pivotal role in the modeling of the Covid-19 epidemics. Furthermore, we discuss ho...