In this episode, we look at two different sides of the online dating game. First, we discuss statistically optimal Tinder strategies with our very own president, Patricio Hernández. Then, we talk to James Davey, the student behind RAW's speed dating algorithm. How close can data science get to predicting love?

In this episode, we have the privilege to talk to Helena Sarin, an engineer turned artist who uses machine learning to create her work. Helena's unique art has been exhibited all over the world; from Zurich, to Dubai, to Miami. In this interview we discuss her artistic process, the intersection of technology and art and society's misconceptions about generative art.

In this episode we talk to David Sumpter a Professor of Applied Mathematics at the University of Uppsala and author of books like "Soccermatics", "Outnumbered" and the "Ten Equations that Rule the World". His mathematical background paired with his passion for football has enabled Dr. Sumpter to consult for clubs and countries like Hammersby, Barcelona and England. Here, we will discuss the different metrics used to analyse football, how professional clubs are embracing data science and what it takes to make it as a football analyst.

As Head of Data Science and Engineering at Walgreens Boots Alliance, Simon Prinn leads a team of over 60 data scientists. In this interview he shares his thoughts on how best to manage and build data science teams, always ensuring to remember the individual in the process.

Grad role or PhD? It's a question troubling many undergraduate and master's students. In this episode, Kenneth Lim talks about his experience undertaking a PhD, the pros and cons, as well as current work as part of IBM's Data Science Elite Team. We also feature a segment by Mathematics and Statistics student Kitti Kovac on the topic of differential privacy.

You may think that the hardest part of statistics is knowing the right techniques, but this skill is worthless if you don't know how to ask the right question in the first place. In this episode, we talk to Professor Jane Hutton, who shares here advice on the topic. We also feature a segment in which we discuss the different types of missing values in data and how to handle them.

In a world increasingly driven by algorithmic decision-making, it is vital that we understand the limitations of such technology. In this episode, Warwick professor Janothan Cave walks through a wide range of examples of algoritms-behaving-badly in health, law, finance, and more. We are feature a segment discussing creativity in machine learning.

It's hard to find a data science degree that doesn't at some point recommend the book "Predictive Analytics". In this episode, we have the pleasure of being joined by Eric Siegel, author of the Amazon category best-selling book, and coordinator of Predictive Analytics World. Breaking up the interview, our Vice-President, Ciaran, discusses the danger of blackbox models in finance.

You've probably heard of Julia, the relatively new programming language promising the speed of C whilst maintaining the simplicity of Python. That said, is it time for you to start learning? In this episode, we talk to data scientist Johannes Smit to find out. We also feature a segment on Goodhart's law introduced through a series of cautionary tales.

In the first episode of DataBasic we discuss what you can expect to see (well, hear) in future episodes and what is currenting going on at WDSS. On top of this there is a segment introducing the basics of Bayesian thinking.