Shows
DataCaféScience Communication with physicist Laurie Winkless, author of "Sticky" & "Science and the City"A key part of the scientific method is communicating the insights to an audience, for any field of research or problem context. This is where the ultimate value comes from: by sharing the cutting-edge results that can improve our understanding of the world and help deliver new innovations in people's lives. Effective science communication sits at the intersection of data, research, and the art of storytelling.In this episode of the DataCafé we have the pleasure of welcoming Laurie Winkless, a physicist, author and science communications expert. Laurie has extensive experience in science journalism, having w...2023-06-0236 minDataCaféA Culture of InnovationCulture 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 scenarios, yet may be unaware of the level of influence that comes from the culture they are part of.In this episode we talk about what it means to design...2022-09-0633 minDataCaféScaling the InternetDo 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 much bandwidth to provision to a town or a city to cope with peak demands? That's where agent-based simulations come in - to allow network designers to anticipate and then plan...2022-07-3045 min
DataCafé[Bite] Documenting Data Science ProjectsDo 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 way, shape, or form. The problem that a lot...2022-06-2916 minDataCaféLanding Data Science Projects: The Art of Change Management & ImplementationAre 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 stakeholder management that influence the chances of landing a project. This is especially important for embedding innovation in your company or organisation, and implementing a plan to sustain t...2022-05-3129 min
DataCafé[Bite] Version Control for Data ScientistsData 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 backupsrecording versionstracking changesexperimenting and testingand working with agility.In this bite episode of the DataCafé we talk about these motivators for version control and how it can strengthen your code d...2022-05-0515 minDataCaféDeep Learning Neural Networks: Building Trust and Breaking BiasWe 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 bias? How can you be sure that your black box is going to adapt to critical new situations?We speak with Prof. Alessio Lomuscio2022-04-0751 min
DataCafé[Bite] Wordle: Winning against the algorithmThe 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 lovely example of the different ways people approach game strategy through their choice of guesses and ways to use the information pres...2022-03-1511 minDataCaféSeries 2 IntroductionLooks like we might be about to have a new Series of DataCafé!Recording date: 15 Feb 2022Intro music by Music 4 Video Library (Patreon supporter)Send us a textThanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.2022-03-1405 min
DataCafé[Bite] Why Data Science projects failData 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 up to their initial potential. In a recent Gartner report, it is anticipated that 85% of Data Science projects will fail to deliver the value they should due to "bias in data...2021-06-2119 minDataCaféData Science for GoodWhat'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 excellent jobs in different industries for which they have a good skills match.We interview Erika Gravina, Rajwinder Bhatoe and Dehaja Senanayake about their story...2021-05-3136 min