Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

Data science sits at the intersection of Computer Science and Statistics, so it comes as no surprise that many of the best data scientists have a computer science or software development background. And those that don’t? Well, there’s a lot they can learn from software developers.In this episode, Ethan Garofolo joins Dr Genevieve Hayes to discuss techniques from software engineering and software development that you can use to become a better data scientist.Guest BioEthan Garofolo is a software developer and software architect, specialising in microservice-based projects and using Lean and DevOps principles to make software development teams more effective. He is the author of Practical Microservices: Build Event-Driven Architectures with Event Sourcing and CQRS and runs the Utah Microservices Meetup group.Talking PointsWhat is the difference between a software engineer, software developer and software architect?The impact of team structure and communications on software design.How Lean and DevOps principles can be used to make technical teams run more effectively.The benefits of pair programming and mob programming.What is test-driven development and how can it be used to enhance the quality of data science outputs?Using ChatGPT/AI to enhance developer capabilities.LinksEthan’s WebsiteConnect with Ethan on LinkedInFollow Ethan on TwitterFollow Ethan on TwitchConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The saying goes that if you’re not paying for the product, then you are the product. And every time you interact with the digital world, there’s a good chance your data is going to be harvested for some alternative use.In this episode of Value Driven Data Science, Dr Kate Bower joins Dr Genevieve Hayes to discuss the data rights of consumers and what data scientists need to be aware of when using consumer data.Guest BioDr Kate Bower is a consumer data advocate for Australian consumer advocacy group CHOICE, following a previous career in academia, where her focus was on qualitative health research.Talking PointsThe rights and responsibilities of consumers and organisations, when it comes to personal data.How organisations currently collect consumer data and what they are using that data for.The use of “harvested” data in AI tools, such as ChatGPT and Stable Diffusion.What data scientists should be aware of when sourcing data for their work.How to source data ethically.LinksConnect with Kate on LinkedInFollow Kate on TwitterCHOICE – Consumers and DataConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

We all want to live long, happy and healthy lives, and in the age of technology, it comes as little surprise that people are turning to data for help.Between smart watches, Oura rings and even just fitness apps like Strava, we’re all generating massive quantities of personal health and fitness data each day, sometimes literally in our sleep. But that data is only valuable if it can be converted into useful insights.In this episode of Value Driven Data Science, Dr Torri Callan joins Dr Genevieve Hayes to discuss how health tech start-ups, such as UAre, are now looking to do just that.This is the third part of a three-part special focussing on the use of data science in start-ups.Guest BioDr Torri Callan is the Data Scientist at Australian health tech start-up UAre, as well as working as a data scientist with fintech start-up Spriggy. He has spent the past 5 years setting up AI and automated risk management for leading finance companies in Australia.Talking PointsHow UAre is using data science to encourage people to exercise more and improve their lives.The challenges of combining data from multiple sources.How to go about building a data product from absolutely nothing.The importance of domain knowledge and research when building a health tech app.What are Bayesian methods and how can they raise the level of rigour of statistical analysis?LinksConnect with Torri on LinkedInUAreConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Once upon a time, data scientists needed to develop programming skills to rival those of software engineers, and this limited the ability of people without such skills to make use of AI. But recently, this has changed, with the huge number of no-code and low-code tools entering the market.In this episode, I’m joined by Geo George to discuss how start-ups are leading the way in leveraging such tools, and in the process, helping to make AI and data science available to all.This is the second part of a three-part special focussing on the use of data science in start-ups.Guest BioGeo George is a director and co-founder of Mayfly Accelerator, a company that helps founders build, grow and scale disruptive start-ups. He is also a start-up founder in his own right and has experience as an executive in the Government sector, with a focus on strategy and risk management.Talking PointsHow are start-ups facilitating the democratisation of AI and data science.The consequences of this democratisation for current and aspiring data scientists.How no code and low code AI and data science tools can be used to develop AI-driven products.The impact of ChatGPT on start-ups, businesses and education in general.LinksConnect with Geo on LinkedInMayfly AcceleratorConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Many data scientists dream of using their skills to develop ground-breaking AI technology. Yet, few manage to translate their dreams into commercially viable products – or even know where to begin. In this episode, start-up founder Dr Jeroen Vendrig joins Dr Genevieve Hayes to discuss his experiences in developing AI-driven products, both in an academic setting and in a variety of organisations within the commercial world.This is the first part of a three-part special focussing on the use of data science in start-ups.Guest BioDr Jeroen Vendrig is the Chief Technology Officer of ProofTec, an Australian technology start-up specialising in the development of AI-driven software for damage detection and assessment of high value assets. He has over 20 years’ experience in video analytics with world leading R&D labs and has over 25 patents in force.Talking PointsThe key differences between doing data science/AI in an academic setting and doing it in the commercial world.How to go about translating academic research into commercially viable AI-based products.What makes for a successful university/commercial collaboration?The challenges of building AI products from scratch, including lack of data and how to tell if a project has the potential to be commercially viable.Protecting IP for AI systems.The impact of having real end users on AI product development.The most valuable skills data scientists can develop for building commercial AI technologies.LinksConnect with Jeroen on LinkedInProofTecConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

AI technology has now reached the point where it can potentially damage the reputation of an organisation, if improperly managed. As a result, many data scientists are now becoming very interested in understanding AI ethics and responsible AI.In this episode of Value Driven Data Science, Chris Dolman joins Dr Genevieve Hayes to discuss strategies organisations and data scientists can apply to de-risk automated decisions, and in doing so, avoid an AI scandal.Guest BioChris Dolman is the Executive Manager, Data and Algorithmic Ethics at Insurance Australia Group, a Gradiant Institute Fellow and regularly contributes to external research on responsible AI and AI ethics. In 2022, he was named the Australian Actuaries Institute’s Actuary of the Year, in recognition of his work around data ethics, and was also included in Corinium Global Intelligence – Business of Data’s list of the Top 100 Innovators in Data and Analytics.Talking PointsThe risks associated with the use or design of AI-based decision-making tools.How these risks might potentially be amplified in the case of new, cutting-edge algorithms, such as ChatGPT.Why “boring” is sometimes better, when it comes to AI.Examples of where things have gone wrong in the past.Strategies for identifying and avoiding potential AI scandals before they occur.The regulation and governance of AI, both now and in the future.LinksConnect with Chris on LinkedInDe-Risking Automated Decisions ReportCheckmate HumanityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The launch of Chat-GPT turned the business world upside down and left many people wondering about the future of their careers. How do you compete against AI? One solution is by delivering a superior customer experience.In this episode, Dasun Premadasa joins Dr Genevieve Hayes to discuss why technical people often trip up when it comes to customer experience and what data scientists can do to overcome these issues.Guest BioDasun Premadasa is the founder of DASCX, an independent business analyst consultancy that helps businesses with their digital transformations and IT project delivery. He is also the host of the DASCX Show on YouTube.Talking PointsHow delivering a superior customer experience can boost your value as a data scientist.Why technical people, such as data scientists, tend to neglect CX.What does good CX look like?The consequences of bad CX for data scientists and end users.The importance of identifying the right customer when pitching a data science solution.Strategies data scientists can employ to improve the experience of their end user.LinksConnect with Dasun on LinkedInDASCX Show episode with Dasun and GenevieveDASCXConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

From social media to electricity grids and the internet itself, we live in a highly interconnected world. But traditional data science techniques don’t adequately allow for the relationships that can exist between data points in such networks. This is where graph data analysis comes into play. In this episode, Dr Alessandro Negro joins Dr Genevieve Hayes to discuss how data scientists can exploit the natural relationships that exist within network datasets through the use of graph-powered machine learning.Guest BioDr Alessandro Negro is the Chief Scientist at GraphAware, the world’s #1 Neo4j consultancy, and Managing Director at GraphAware Italy. He is also the author of Graph-Powered Machine Learning and the recently released Knowledge Graphs Applied.Talking PointsWhat is graph data and how does it differ from structured data?Use cases for graphs and graph databases?What is a knowledge graph, how are they created and what are their benefits?How can graphs be used to power machine learning?How can machine learning algorithms be used to build knowledge graphs?Steps data scientists can take to get started with graph data science and knowledge graphs.LinksConnect with Alessandro on LinkedInGraph-Powered Machine LearningKnowledge Graphs AppliedConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data science is an in-demand skill. Yet, many data scientists find it challenging to get started in the industry and to differentiate themselves from other data scientists once they find a job.In this episode, Jonathan Stark joins Dr Genevieve Hayes to discuss how data scientists can find their niche and build a reputation as a data science authority.Guest BioJonathan Stark is a former software developer who now helps independent professionals make a living while increasing their impact on the world. He is the author of Hourly Billing Is Nuts, the host of the podcast Ditching Hourly and the co-host of The Business of Authority.Talking PointsHow data scientists can build their authority and move away from being viewed as commodities.The benefits of specialisation, both as an independent professional, and as an employee of a larger organisation.Is it necessary to manage staff in order to establish your credibility as an authority in data science?How your authority as a data scientist, once established, could potentially be leveraged both within the corporate world and as a springboard into an independent career.LinksJonathan’s WebsiteDitching HourlyThe Business of AuthorityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

“Data science unicorns” are those rare people who “understand the (data) problem they seek to resolve, have the mathematical expertise to analyse the problem and possess the computing skills to covert this knowledge into outcomes.” In fact, they are considered so rare that some people have suggested they don’t really exist. Yet, although nobody is born a data science unicorn, organisations with the right know-how can create them.In this episode, Dr Peter Prevos joins Dr Genevieve Hayes to discuss his work in creating data science unicorns from water industry subject matter experts around the world.Guest BioDr Peter Prevos is a civil engineer, social scientist (and amateur magician) who manages the data science function at Coliban Water in regional Australia and runs courses in data science for water professionals. He is also the author of a number of books including Principles of Strategic Data Science and the recently released Data Science for Water Utilities.Talking PointsWhy Linux is the best operating system for data science.How the social sciences can make you a better data scientist.Creating data science unicorns in the water industry.The similarities and differences between data science in the water industry and in other industries.What data scientists can learn from the world of theatrical magic.LinksConnect with Peter on LinkedIn Peter’s websiteComputer Magic: Software Illusions and DeceptionsComputer Science 4 FunConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Are you familiar with “environmental justice”? It’s all about equitable access to environmental amenities and the equitable distribution of pollution, and has its roots in the American Civil Rights movement of the 1960’s and 1970’s.In this episode, Robin Rotman and Amber Spriggs join Dr Genevieve Hayes to discuss the environmental justice movement and how open access GIS-based tools are being used to achieve environmental justice in the USA today.Guest BioRobin Rotman is an Assistant Professor of Energy and Environmental Law and Policy at the University of Missouri-Columbia. She is also a qualified lawyer, focussing on energy, environmental, and natural resource issues, and is a Counsel at Van Ness Feldman, a law firm in Washington DC.Amber Spriggs is a civil engineering Masters student at the University of Missouri-Columbia with a research focus on hydrology, hydraulic engineering, GIS-based risk assessment, and flood insurance policy.Talking PointsWhat is environmental justice?Why the environmental justice movement and the American Civil Rights movement are one and the same.The role of data and analytics in achieving environmental justice both now and when the term was first coined.Examples of how spatial data analysis has been used to achieve environmental justice.How similar techniques could potentially be used to achieve positive outcomes for the community in other ways.The role of data and analytics in legal proceedings relating to environmental justice.LinksConnect with Robin on LinkedInConnect with Amber on LinkedInEJScreenConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data scientists are constantly being told of the importance of effective communication for their career success. But this advice typically translates to being able to communicate effectively the results of their work. One aspect of communication that is often overlooked is conversational communication.In this episode, Julia Lessing joins Dr Genevieve Hayes to discuss the skills and techniques data scientists can combine to make their workplace conversations a lot easier.Guest BioJulia Lessing is the principal actuary and Director of Guardian Actuarial, which specialises in helping clients use data to solve complex people-oriented problems, and runs the Guardian Actuarial Leadership Program and the Easier Conversations course. She is also the host of the We Are Actuaries podcast and has trained and served as a Lifeline phone counsellor.Talking PointsThe importance of effective conversations in the workplace.Common stumbling blocks for technical people, when it comes to effective communication.The potential consequences of poor workplace conversations and the benefits of good conversations.The key skills involved in conducting an effective conversation.How you go about preparing for important conversationsConducting effective conversations within large groups and how to make meeting communications more effective.LinksConnect with Julia on LinkedInEasier ConversationsConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In December 2022, OpenAI released ChatGPT for public testing and within a week of its launch, the user count exceeded 1 million. For many, ChatGPT provided a first glimpse at what an AI-powered future might look like.In this episode, Dr Genevieve Hayes is joined once again by Dr David Joyner to discuss the implications of AI-driven technology, such as ChatGPT, for education, business and the world in general, and to finish their discussion of Georgia Tech’s OMSCS program.This is the second part of a two-part conversation, which began in Episode 9.Guest BioDr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.Talking PointsThe evolution of human computer interaction.The role of Masters programs vs shorter courses in helping data scientists keep up with the latest technological developments.Why ChatGPT is a game changer for education and business in general.The opportunities and challenges presented by AI chatbots, such as ChatGPT.The importance of understanding the context when analysing data.LinksDavid’s websiteGeorgia Tech’s OMSCSConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

What if you could get a Masters degree in Machine Learning for under US$8000, from a top US university, without quitting your day job or moving location? Georgia Tech’s pioneering Online Master of Science in Computer Science (OMSCS) program offers just that. In this episode, Dr David Joyner joins Dr Genevieve Hayes to discuss OMSCS, the world’s first MOOC-based degree.This is the first part of a two-part conversation, which is continued in Episode 10.Guest BioDr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.Talking PointsWhat is the OMSCS?How OMSCS compares to other Computer Science Masters programs and MOOCs?How online education can help data scientists keep pace with the rapidly changing technological landscape.Challenges and opportunities associated with teaching and learning in the online space.Why Georgia Tech students talk about “getting out” instead of “graduating”.LinksDavid’s websiteGeorgia Tech’s OMSCSConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Ever since Facebook rebranded itself as Meta, the term “metaverse” has entered everyone’s vocabulary, but there’s still a lot of confusion about what it actually is and how it’s likely to affect our lives in the future. In this episode, Romeo Cabrera Arévalo, a data scientist working in the immersive technology space, joins Dr Genevieve Hayes to answer these questions and more.Guest BioRomeo Cabrera Arévalo is a senior AI and computer vision researcher and engineer at Immersed, “the world’s first professional metaverse.” He is also an AI and tech advisor to the Board of Laboratorio iA, and has lectured in the Masters of Data Science program at the Escuela Superior Politéchnica del Litoral.Talking PointsWhat is the metaverse and why should people care?The difference between virtual reality, augmented reality and mixed reality.The potential benefits of immersive technologies, both now and in the future.The role of AI, data science and machine learning in the metaverse.The types of algorithms and techniques that go into building metaverse technologies.The “uncanny valley” and the challenge of giving avatars legs in the metaverse.LinksConnect with Romeo on LinkedInImmersedConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Over the past decade, demand for data talent has grown exponentially, and this has had a massive impact on talent acquision in the data space. Employers of data professionals frequently cite talent acquisition as one of the biggest challenges they face in building their internal data capabilties. In this episode, Dr Genevieve Hayes is joined by data recruiter Joel Robinstein to discuss the data science recruitment landscape, including practical advice for both data scientists and those looking to employ them.Guest BioJoel Robinstein is Head of Clients Services and Operations at Precision Sourcing Australia, where he has over 12 years’ experience working in the data recruitment space. He is also the co-host of the podcast Keeping Up With Data.Talking PointsThe evolution of the data science recruiting space over the last 10 years.What employers look for when hiring data staff, what data scientists look for in prospective employers, and how this varies by role seniority.Strategies organisations can employ to identify, attract and retain data talent.What makes a great data science leader.Career paths available to data scientists and how you can make them happen.LinksConnect with Joel on LinkedInKeeping up with Data podcastConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The success of data science projects often depends on being able to get stakeholders, from a variety of backgrounds, to work well together. But what if the stakeholders involved come from very different backgrounds and struggle to understand each other – as can be the case with data scientists and engineers? In this episode, Dr Genevieve Hayes is joined by software engineer turned data scientist Hendrik Dreyer, who has carved a niche for himself by acting as a intermediary between Team Data Science and Team Engineering.Guest BioHendrik Dreyer is both a qualified data scientist and a qualified engineer. He worked extensively in a range of senior software engineering roles, in both South Africa and Australia, prior to making the transition into data science. He is now the Manager of Analytics Capability at Australia’s largest superannuation fund, AustralianSuper.Talking PointsThe different mindsets commonly held by data scientists, data engineers and the business in general, when it comes to data science and analytics.How these diverse mindsets can give rise to challenges, when it comes to delivering data science solutions, and the potential consequences if these challenges aren’t adequately addressed.Approaches to bridging the chasm between data science, data engineering and the business.Actions each of these different groups of people can take in order to help bridge the divide within their own organisations.The unexpected benefit of agile project management as a people development tool.LinksConnect with Hendrik on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Businesses rarely approach data scientists with well-defined problems to solve. Sometimes, the problems businesses devise aren’t appropriate for solving using data science at all. This makes it difficult for data science projects to succeed. In this episode, Dr Genevieve Hayes is joined by Rob Deutsch to discuss strategies businesses and data scientists can employ to identify data science use cases and maximise their probability of success.Guest BioRob Deutsch is the Chief Operating Officer of AkuShaper, a company that uses advanced modelling algorithms and software to build better surfboards faster. He is also a data science consultant with Parity Analytic, and previously founded Boxer, which built software for creating better financial models.Talking PointsProcesses for identifying and understanding business problems, and determining whether a data science solution is appropriate and what that solution should look like.The different ways in which people from different backgrounds can look at a data science problem and how that influences the questions they ask of data/the way they tackle problems.The role of the business vs the role of the data scientist in defining/scoping data science projects.How to maximise the probability of success of a data science project.How the data science/data analytics skill set can be transferred to areas outside of technical data analysis, such as running a SaaS company.LinksConnect with Rob on LinkedInBird App XKCD ComicConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Have you ever wondered what your organisation’s Board are thinking, when it comes to data use? In this episode, Dr Genevieve Hayes is joined by Dr Stuart Black to discuss the attitudes of Boards to data use and their implications for the organisations they govern.Guest BioDr Stuart Black is an Enterprise Fellow in data, analytics, disruption and innovation at the University of Melbourne. Prior to joining academia, Stuart spent 30 years in professional services and industry, at employers including Deloitte, where he was Senior Partner, National Australia Bank and AT Kearney. He is also a co-author of the recently released book Business Model Transformation – the AI and Cloud Technology Revolution.Talking PointsThe Board’s role in catalysing and controlling data-driven business model transformation.What is meant by the secondary use of data and what are some of the opportunities and threats presented by it?Why intellectual curiosity is more important than prior data experience in maximising the competitive advantage of data.The importance of taking a medium term view when it comes to data initiatives.The key Board attributes that determine an organisation’s attitudes towards data as an enabler of strategy.Strategies for shifting the attitude of your Board in order to encourage a mindset that is more supportive of data initiatives.LinksConnect with Stuart on LinkedInStuart’s University of Melbourne ProfileBusiness Model Transformation – the AI and Cloud Technology Revolution MicrositeConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

We all know what it means for a human to discriminate against another human, but the concept of a predictive model or an artificial intelligence is relatively new. What does it mean for a model or an AI to discriminate against someone? In this episode of Value Driven Data Science, Dr Genevieve Hayes is joined by Dr Fei Huang to discuss the importance of considering fairness and avoiding discrimination when developing machine learning models for your business.Guest BioDr Fei Huang is a senior lecturer in the School of Risk and Actuarial Studies at the University of New South Wales, who has won awards for both her teaching and her research. Her main research interest is predictive modelling and data analytics, and has recently been focussing on insurance discrimination and pricing fairness.Talking PointsDirect vs indirect discrimination and how data scientists can create discriminatory machine learning models without ever intending to.What it means for a model to be fair and the trade-off that exists between individual and group fairness.How fairness and discrimination come up (and have been addressed) in different applications of machine learning, including (but not limited to) insurance.How different jurisdictions are currently addressing algorithmic discrimination, through regulation and other means.What this means for organisations who currently make use of machine learning models or would like to in the future.Why organisations should start considering fairness and discrimination when using analytics and what they can do about it now.LinksConnect with Fei on LinkedInFei’s UNSW Public ProfileAustralia’s Voluntary Ethical AI FrameworkFei’s papers on fairML and insurance pricing:Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria and ModellingThe Discriminating (Pricing) ActuaryWelfare Implications of Fairness and Accountability for Insurance PricingConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The two most challenging transitions you can make in your career are transitioning from individual contributor to team lead, and moving from team lead to managing managers. This is true across all professions, but is particularly pronounced in technical fields, like data science. In this episode, host Dr Genevieve Hayes is joined by guest Tim Davey to discuss the challenges faced by data scientists looking to climb the corporate ladder, and how employers of data professionals can support them in developing their careers.Guest BioTim Davey has spent the majority of his career working in the organisational development and HR space where his work has focussed strongly on the development of leaders and working with individuals to understand and maximise their careers. This has included, among other things, providing executive coaching to senior management across a wide range of industries, including media, the performing arts, manufacturing, financial services, transport, education, insurance, legal, and not-for-profit sectors. Yet, Tim also has a strong technical background himself, having completed a Science degree at the University of Melbourne, and starting his working career in the chemical manufacturing sector, so has first-hand understanding of the challenges faced by the members and leaders of technical teams.Talking PointsThe key differences between working as an individual contributor vs line manager vs senior manager.Why people can struggle to make the transition between data scientist and team lead and what can be done to make it easier.The importance of technical capability vs managerial skills in technical leadership roles, and how organisations can support staff to develop those skill sets if one is lacking or weaker.Managing a team and building your profile in the post-COVID, remote working world.Advice for data scientists considering moving into managerial roles – and for those who would prefer to remain an individual contributor.LinksConnect with Tim on LinkedInConnect with Genevieve on LinkedInDownload the FREE Data Science Project Discovery GuideGenevieve Hayes Consulting offers one-on-one coaching for new and aspiring data science and analytics leaders. To find out more, or to share your thoughts and feedback on the podcast, you can get in touch here.Be among the first to hear about the release of each new podcast episode by signing up HERE

Data presents incredible opportunities for organisations to create value, but with the current skills and labour shortages that are affecting all businesses, finding and retaining data scientists and other data professionals can be hard. In this episode, host Dr Genevieve Hayes is joined by guest Amanda Aitken to discuss a practical way in which organisations can address the skills shortage, gain much needed data skills and increase staff retention – by upskilling their existing staff.Guest BioAmanda Aitken is a fully-qualified actuary who is currently an educator with the Actuaries Institute of Australia. She teaches data analytics and data science to actuaries through the Actuaries Institute’s Data Analytics Application course and is also a member of the Institute’s Data Analytics Practice Committee and Data Analytics Education Faculty.Talking PointsThe difference between an actuary and a data scientist.Why data skills are becoming increasingly important and the benefits to organisations of upskilling their data team.What’s involved in upskilling data-focussed staff.The importance of considering “soft skills”, such as communication, privacy and ethics, when training data scientists.How an organisation can get the most out of their data staff once they have been upskilled.LinksConnect with Amanda on LinkedInData Science Applications MicrocredentialData Analytics SeminarMachines Behaving Badly by Toby WalshHow Humans Judge Machines by Cesar HidalgoDownload the FREE Data Science Project Discovery GuideConnect with Genevieve on LinkedInTo find out more about custom data science training from Genevieve Hayes Consulting or to share your thoughts and feedback on the podcast, you can get in touch HERE.Be among the first to hear about the release of each new podcast episode by signing up HERE