DataCafé

Culture is a key enabler of innovation in an organisation. Culture underpins the values that are important to people and the motivations for their behaviours. When these values and behaviours align with the goals of innovation, it can lead to high performance across teams that are tasked with the challenge of leading, inspiring and delivering innovation. Many scientists and researchers are faced with these challenges in various sce...

Do you have multiple devices connected to your internet fighting for your bandwidth? Are you asking your children (or even neighbours!) to get off the network so you can finish an important call? Recent lockdowns caused huge network contention as everyone moved to online meetings and virtual classrooms. This is an optimisation challenge that requires advanced modelling and simulation to tackle. How can a network provider know how m...

Do you ever find yourself wondering what the data was you used in a project? When was it obtained and where is it stored? Or even just the way to run a piece of code that produced a previous output and needs to be revisited?

Chances are the answer is yes. And it’s likely you have been frustrated by not knowing how to reproduce an output or rerun a codebase or even who to talk to to obtain a refresh of the data - in some wa...

Are people resistant to change? And if so, how do you manage that when trying to introduce and deliver innovation through Data Science?

In this episode of the DataCafé we discuss the challenges faced when trying to land a data science project. There are a number of potential barriers to success that need to be carefully managed. We talk about "change management" and aspects of employee behaviours and stakeho...

Data scientists usually have to write code to prototype software, be it to preprocess and clean data, engineer features, build a model, or deploy a codebase into a production environment or other use case. The evolution of a codebase is important for a number of reasons which is where version control can help, such as:

• collaborating with other code developers (due diligence in coordination and delegation)
• generating backups
• record...

We explore one of the key issues around Deep Learning Neural Networks - how can you prove that your neural network will perform correctly? Especially if the neural network in question is at the heart of a mission-critical application, such as making a real-time control decision in an autonomous car. Similarly, how can you establish if you've trained your neural network at the heart of a loan decision agent  with a prebuilt bia...

The grey, green and yellow squares taking over social media in the last few weeks is an example of the fascinating field of study known as Game Theory.  In this bite episode of DataCafé we talk casually about Wordle - the internet phenomenon currently challenging players to guess a new five letter word each day.

Six guesses inform players what letters they have gotten right and if they are in the right place. It’s a lovel...

Data Science in a commercial setting should be a no-brainer, right? Firstly, data is becoming ubiquitous, with gigabytes being generated and collected every second. And secondly, there are new and more powerful data science tools and algorithms being developed and published every week. Surely just bringing the two together will deliver success...

In this episode, we explore why so many Data Science projects fail to live u...

What's the difference between a commercial data science project and a Data Science project for social benefit? Often so-called Data Science for Good projects involve a throwing together of many people from different backgrounds under a common motivation to have a positive effect.

We talk to a Data Science team that was formed to tackle the unemployment crisis that is coming out of the pandemic and help people to find ...

The scientific method consists of systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses. But what does this mean in the context of Data Science, where a wealth of unstructured data and variety of computational models can be used to deduce an insight and inform a stakeholder's decision?

In this bite episode we discuss the importance of the scientific method fo...

On 30 July 2020 NASA launched the Mars 2020 mission from Earth carrying a rover called Perseverance, and rotorcraft called Ingenuity, to land on and study Mars. The mission so far has been a resounding success, touching down in Jezero Crater on 18 February 2021, and sending back data and imagery of the Martian landscape since then.

The aim of the mission is to advance NASA's scientific goals of establishing if there w...

Welcome to the first DataCafé Bite: a bite-size episode where Jason and Jeremy drop-in for a quick chat about a relevant or newsworthy topic from the world of Data Science. In this episode, we discuss how to hire a great Data Scientist, which is a challenge faced by many companies and is not easy to get right.

From endless coding tests and weird logic puzzles, to personality quizzes and competency-based interviews; there a...

In this episode we talk about all things Bayesian. What is Bayesian inference and why is it the cornerstone of Data Science?

Bayesian statistics embodies the Data Scientist and their role in the data modelling process. A Data Scientist starts with an idea of how to capture a particular phenomena in a mathematical model - maybe derived from talking to experts in the company. This represents the prior belief about the model....

Have you ever come home from the supermarket to discover one of the apples you bought is rotten? It's likely your trust for that grocer was diminished, or you might stop buying that particular brand of apples altogether.

In this episode, we discuss how the quality controls in a production line need to use smart sampling methods in order to avoid sending bad products to the customer, which could ruin the reputation of...

As we look ahead to a new year, and reflect on the last, we consider how data science can be used to optimise the future. But to what degree can we trust past experiences and observations, essentially relying on historical data to predict the future? And with what level of accuracy?

In this episode of the DataCafé we ask: how can we optimise our predictions of future scenarios to maximise the benefit we can obtain from th...

What exciting data science problems emerge when you try to forecast an election? Many, it turns out!

We're very excited to turn our DataCafé lens on the current Presidential race in the US as an exemplar of statistical modelling right now. Typically state election polls are asking around 1000 people in a state of maybe 12 million people how they will vote (or even if they have voted already) and return a predictive re...

What are solar storms? How are they caused? And how can we use data science to forecast them?

In this episode of DataCafé we talk about the Sun and how it drives space weather, and the efforts to forecast solar radiation storms that can have a massive impact here on Earth.

On a regular day, the Sun has a constant stream of charged particles, or plasma, coming off its surface into the solar system, known as the so...

How do you get your latest and greatest data science tool to make an impact? How can you avoid wasting time building a supposedly great data product only to see it fall flat on launch?

In this episode, we discuss how you need to start with the idea before you get to a data product. As all good entrepreneurs know, if you can't sell the idea, you're certainly not going to be able to sell the product. We take inspir...