Value Driven Data Science

Genevieve Hayes Consulting Episode 32: Blockchain and Cryptocurrency for Data Science

Depending on who you speak to blockchain and cryptocurrency are either the way of the future or the scam of the century. But few would be able to tell you what either of them actually is – including among data scientists for whom data and technology are a way of life. 

In this episode, Luke Willis joins Dr Genevieve Hayes to demystify blockchains, cryptocurrency and the data behind them.

Guest Bio

Luke Willis is the dApp UX guy. He’s a web3 developer with extensive front end and UX experience. He’s also the founder of the Koin Press where he writes a regular newsletter, hosts the Koin Press podcast and helps others make their dApp ideas a reality.

Talking Points

• What is the blockchain?
• The different types of blockchains and the differences between them?
• How the blockchain relates to cryptocurrency.
• What is a dApp and Luke’s experiences in building them.
• How data is stored on the blockchain and how it can be accessed.

Links

• Luke’s Website
• Koinos Blocks
• Koiner
• Etherscan

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 32: Blockchain and Cryptocurrency for Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 31: The Business Leader as Data Consumer

When data science first became the must-have skill of the 21st century, organisations were fighting to recruit the best and brightest data science talent. But the glory of having a data scientist on staff was often short-lived, as many organisations soon found they didn’t know what to do with them.

Business leaders had been sold the dream of being able to turn their data into business gold but were unable to maximise the value of the data science expertise they had brought in because they couldn’t communicate effectively with their new data science teams.

In this episode, Dr Howard Friedman joins Dr Genevieve Hayes to discuss how adopting a customer mindset can help business leaders capitalise on the hidden value of data.

Guest Bio

Dr Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modelling teams in the private sector, public sector and academia. He is an adjunct professor, teaching data science, statistics, and program evaluation, at Columbia University, and has authored/co-authored over 100 scientific articles and book chapters in areas of applied statistics, health economics and politics. His previous books include Ultimate Price and Measure of a Nation, which Jared Diamond called the best book of 2012.

Talking Points

• How Howard’s personal experiences informed the writing of Winning with Data Science.
• What business leaders should know, in order to be effective customers of data science teams.
• How important is it for business leaders to be up to date with the latest data science trends and buzzwords?
• What data scientists should know in order to work more effectively with business leaders.
• Howard’s previous book, Ultimate Price.
• How data scientists and economists go about placing a price on human life.

Links

• Connect with Howard on LinkedIn
• Howard’s website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 31: The Business Leader as Data Consumer first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 30: Cause and Effect Data Science

Correlation does not equal causation, as anyone who has studied statistics or data science would know. But understanding causality isn’t just important when you’re developing models.

If you’re working in business and want to be recognised for your work, it’s essential to be able to demonstrate causality between what you do and the benefit flowing through to the business.

In this episode, Mark Stouse joins Dr Genevieve Hayes to discuss how data science can be used to comprehend the underlying cause-and-effect relationships in business data.

Guest Bio

Mark Stouse is the CEO of Proof Analytics, an AI-driven marketing analytics platform. Prior to becoming an analytics software CEO, Mark had a successful career in B2B marketing and in 2014 was named Innovator of the Year at the Holmes Report In2 SABRE Awards for his work in tying marketing and communication investment to key business performance metrics.

Talking Points

• The benefits to organisations of understanding causality.
• How such techniques can be applied to use cases and disciplines beyond marketing analytics.
• How data scientists can drive conversations about analytics at the C-suite level to maximise their impact.
• The potential future impact of generative AI on data science and the world in general.

Links

• Connect with Mark on LinkedIn
• Proof Analytics

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 30: Cause and Effect Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 29: Creating Order From Data Chaos

The insurance sector owes its existence to data and insurers were some of the first companies to utilise data expertise. Yet, being an early adopter isn’t always as great as it seems. And many big insurers are now discovering the challenges of bringing their long-established data systems into the 21st century.

In this episode, Maria Ferrés joins Dr Genevieve Hayes to discuss the complexities of creating order from data chaos in the insurance industry.

Guest Bio

Maria Ferrés is an actuary with extensive experience throughout Europe and Australia, who now specialises in establishing the enterprise data functions of multinational insurers. She is currently the Enterprise Data Officer at trade credit insurer Atradius and she also advises companies within the insurtech space on the use of data to comply with Data Protection laws.

Talking Points

• The ideal state of an insurer’s enterprise data capabilities.
• How to transform insurers’ data capabilities from their present, often chaotic state, to this ideal.
• The challenges in transforming insurers’ data capabilities.
• Where data scientists fit into the transformation process.
• How to overcome resistance encountered while transforming the data capabilities of an organisation.
• The impact of the GDPR on enterprise data capabilities and on the work of people using insurance data, including data scientists and Insurtechs.

Links

• Connect with Maria on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 29: Creating Order From Data Chaos first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 28: The Data Science Behind ChatGPT

ChatGPT was one of the best things to ever happen to data science – not so much because of what it can do, but because, virtually overnight, it made AI and data science mainstream. 

However, while most data scientists now have experience with ChatGPT and other large language model (LLM)-based technologies as end users, few have had experience in building their own LLM-based tools.

In this episode, Dr Mudasser Iqbal joins Dr Genevieve Hayes to discuss the data science behind LLMs and how to go about doing just that.

Guest Bio

Dr Mudasser Iqbal is the Founder and CEO of TeamSolve, a company dedicated to leveraging AI for digital transformation with a sustainable focus. He has extensive experience in Industrial AI, including multiple patents, and was recognised as an MIT Young Innovator. He also played a key role in the growth of his previous start-up, Visenti, and its subsequent acquisition by Xylem Inc.

Talking Points

• The data science behind LLMs.
• How TeamSolve’s Lily, compares to ChatGPT and the advantages of a domain-specific, private chatbot, such as Lily, over a more general, public chatbot, such as ChatGPT.
• How knowledge graphs can be combined with LLMs to overcome many of the shortcomings of LLMs.
• The changing attitudes of organisations around the use of generative AI tools.
• What the emergence of cutting-edge AI tools, such as LLMs, mean for more traditional data science tools, such as analytics dashboards.
• The future of generative AI, and the potential benefits and risks to society.

Links

• TeamSolve
• Connect with Mudasser on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 28: The Data Science Behind ChatGPT first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 27: The Future of Technology in Financial Services

Despite its conservative reputation, the financial services industry has always been a big adopter of cutting-edge technologies. Dating back more than a century, it’s also been one of the biggest employers of people with technology and data-related skills. But what does the future hold for the use of tech in the financial services industry?

In this episode, Ben Shapira joins Dr Genevieve Hayes to discuss what this future might look like and how technology is being used right now to improve the lives of consumers.

Guest Bio

Ben Shapira is a digital strategist and UX specialist turned tech entrepreneur. He is the founder and Chief Product Officer of Australian fintech start-up Dinero, as well as being a lecturer in the Master of Media and Communication program at Swinburne University.

Talking Points

• Where the financial services industry is heading, regarding the use of technology and how this will affect the lives of consumers.
• The types of data modelling and analysis that are possible because of the data produced by these new technologies.
• What is Dineiro and how data informed its creation.
• The impact of data security considerations on financial services organisations’ ability to adopt new technologies and make use of the data they produce.
• Advice for data scientists looking to build a career in marketing and advertising.
• How marketing techniques can be applied to data science to make data scientists more effective, regardless of their industry.

Links

• Connect with Ben on LinkedIn
• Dineiro

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 27: The Future of Technology in Financial Services first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 26: Data Storytelling and Data-Informed Education

Data science is only useful if it can create value. And one way that value can be created is by using data to influence decision-making. Yet, to influence decisions, data scientists need to effectively communicate the outcomes of their work – which is something many struggle with. This is because effective data science communication is about more than just rattling off statistics and expecting your end users to piece them together.

In this episode, Dr Selena Fisk joins Dr Genevieve Hayes to discuss how data scientists can improve their communication by using those numbers to tell a story.

Guest Bio

Dr Selena Fisk is a data storyteller and researcher, with a background in education, who now works with the corporate sector to develop data-informed strategies. She is also the author of a number of books, including I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World and Data-Informed Learners: Engaging Students in their Data Story.

Talking Points

• What is data storytelling and how does it differ from data visualisation?
• How can data scientists make use of storytelling techniques to maximise the impact of their work?
• The difference between being data-informed and data-driven, and what that means for schools and businesses.
• How data is being used in schools to inform learning and improve educational outcomes.
• How educators can involve students in the data conversation, and what data scientists can learn from this when it comes to engaging business stakeholders in their work.

Links

• Selena’s Website
• Connect with Selena on LinkedIn
• Follow Selena on Twitter

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 26: Data Storytelling and Data-Informed Education first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 25: The Risks of Applying Data Science to Financial Modelling

Pretty much everyone has a retirement plan, but those plans aren’t always robust enough to see you through to the finish line of life. And part of that is a direct consequence of incorrectly applying data science principles to financial modelling.

In this episode, Todd Tresidder joins Dr Genevieve Hayes to discuss the risks and limitations of using data science when planning for retirement.

Guest Bio

Todd Tresidder is a former hedge fund manager who “retired” at age 35 to become a financial consumer advocate and money coach. He now runs the popular retirement planning website FinancialMentor.com and is the author of a range of books on retirement planning and investments including How Much Money Do I Need to Retire? and The Leverage Equation.

Talking Points

• What are some of the limitations of traditional financial modelling?
• Examples of what can happen when traditional financial modelling goes very wrong.
• How to do financial modelling the right way.
• The Engineer’s Fallacy or why you shouldn’t apply pure data science to financial planning.
• The implications of this for fields outside of the financial services industry.

Links

• Todd’s Website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 25: The Risks of Applying Data Science to Financial Modelling first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 24: AI and IP

If you look at the list of the greatest inventions of the 20th century, you’ll find they all have two things in common. From tea bags to toasters and from cell phones to cellophane, they all take the form of physical objects, and all are, or at least were, protected by patents.

Yet, since the turn of the century, the nature of inventions has changed significantly. And many of the greatest inventions of this century now take the form of computer code or models.

But how do you protect an invention you can’t physically touch?

In this episode, Helen McFadzean joins Dr Genevieve Hayes to discuss the intersection of artificial intelligence and intellectual property.

Guest Bio

Helen McFadzean is a patent and trademark attorney, with a background in artificial intelligence and mechatronics engineering. She has successfully obtained patents, trademarks and designs for businesses in Australia and overseas in a large number of technology areas including machine learning and image classification, automation, smart devices, audio signal processing, embedded software, and control systems.

Talking Points

• What is the difference between patents, trademarks and copyrights?
• How do you know if an AI/ML-based invention is worth protecting and how do you protect it if it is?
• What parts of an AI/ML-based invention can be protected through patent law?
• The importance of good communication in capturing IP.
• What happens if an invention was invented by a generative AI, rather than a human?

Links

• Connect with Helen on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 24: AI and IP first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 23: Reinforcement Learning – The Other Type of Machine Learning

Most Intro to Machine Learning courses cover supervised learning and unsupervised learning. But did you know there is also a third type of machine learning, which was used in the development of ChatGPT and is likely to become increasingly important in the not too distant future?

In this episode, Prof Michael Littman joins Dr Genevieve Hayes to discuss reinforcement learning – the other type of machine learning – as well as his new book, Code to Joy: Why Everyone Should Learn a Little Programming.

Guest Bio

Prof. Michael Littman is an award-winning Professor of Computer Science at Brown University, specialising in reinforcement learning; is co-creator of the Machine Learning and Reinforcement Learning courses offered as part of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program; and is currently serving as Division Director for Information and Intelligent Systems at the (US) National Science Foundation. He is also the author of Code to Joy: Why Everyone Should Learn a Little Programming.

Talking Points

• What is reinforcement learning and why has it traditionally been seen as “the other type of machine learning”?
• Current and future applications of reinforcement learning.
• How reinforcement learning is being used to create business value.
• Michael’s new book, Code to Joy and why everyone should learn to code.
• How non-programmers can get started with coding and what it would mean for the world if more people did code.

Links

• Michael’s Website
• Follow Michael on Twitter
• Computing Up Podcast
• Machine Learning A Cappella (Thriller Parody)

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 23: Reinforcement Learning – The Other Type of Machine Learning first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 22: Software Engineering for Data Science

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 Bio

Ethan 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 Points

• What 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.

Links

• Ethan’s Website
• Connect with Ethan on LinkedIn
• Follow Ethan on Twitter
• Follow Ethan on Twitch

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 22: Software Engineering for Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 21: Responsible Data Sourcing for AI Model Building

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 Bio

Dr 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 Points

• The 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.

Links

• Connect with Kate on LinkedIn
• Follow Kate on Twitter
• CHOICE – Consumers and Data

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 21: Responsible Data Sourcing for AI Model Building first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 20: Using Data Science to Live Better for Longer

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 Bio

Dr 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 Points

• How 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?

Links

• Connect with Torri on LinkedIn
• UAre

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 20: Using Data Science to Live Better for Longer first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 19: The Democratisation of AI and Data Science

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 Bio

Geo 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 Points

•  How 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.

Links

• Connect with Geo on LinkedIn
• Mayfly Accelerator

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 19: The Democratisation of AI and Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 18: Making AI Commercially Viable

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 Bio

Dr 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 Points

• The 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.

Links

• Connect with Jeroen on LinkedIn
• ProofTec

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 18: Making AI Commercially Viable first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 17: How to Avoid an AI Scandal

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 Bio

Chris 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 Points

• The 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.

Links

• Connect with Chris on LinkedIn
• De-Risking Automated Decisions Report
• Checkmate Humanity

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 17: How to Avoid an AI Scandal first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 16: Improving the Data Science Customer Experience

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 Bio

Dasun 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 Points

• How 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.

Links

• Connect with Dasun on LinkedIn
• DASCX Show episode with Dasun and Genevieve
• DASCX

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 16: Improving the Data Science Customer Experience first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 15: Graph-Powered Data Science

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 Bio

Dr 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 Points

• What 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.

Links

• Connect with Alessandro on LinkedIn
• Graph-Powered Machine Learning
• Knowledge Graphs Applied

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 15: Graph-Powered Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 14: Building Your Authority in Data Science

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 Bio

Jonathan 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 Points

• How 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.

Links

• Jonathan’s Website
• Ditching Hourly
• The Business of Authority

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 14: Building Your Authority in Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 13: Breeding Data Science Unicorns

“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 Bio

Dr 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 Points

• Why 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.

Links

• Connect with Peter on LinkedIn
• Peter’s website
• Computer Magic: Software Illusions and Deceptions
• Computer Science 4 Fun

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 13: Breeding Data Science Unicorns first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 12: The Role of Data in Environmental Justice

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 Bio

Robin 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 Points

• What 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.

Links

• Connect with Robin on LinkedIn
• Connect with Amber on LinkedIn
• EJScreen

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 12: The Role of Data in Environmental Justice first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 11: Better Workplace Conversations for Data Scientists

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 Bio

Julia 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 Points

• The 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 conversations
• Conducting effective conversations within large groups and how to make meeting communications more effective.

Links

• Connect with Julia on LinkedIn
• Easier Conversations

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 11: Better Workplace Conversations for Data Scientists first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 10: ChatGPT and the Future of Human Computer Interaction

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 Bio

Dr 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 Points

• The 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.

Links

• David’s website
• Georgia Tech’s OMSCS

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 10: ChatGPT and the Future of Human Computer Interaction first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 9: Learning Data Science at Scale with OMSCS

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 Bio

Dr 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 Points

• What 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”.

Links

• David’s website
• Georgia Tech’s OMSCS

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 9: Learning Data Science at Scale with OMSCS first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 8: Data Science in the Metaverse

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 Bio

Romeo 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 Points

• What 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.

Links

• Connect with Romeo on LinkedIn
• Immersed

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 8: Data Science in the Metaverse first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 7: Finding and Retaining the Best Data Talent

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 Bio

Joel 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 Points

• The 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.

Links

• Connect with Joel on LinkedIn
• Keeping up with Data podcast

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 7: Finding and Retaining the Best Data Talent first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 6: Bridging the Chasm Between Data Science and Engineering

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 Bio

Hendrik 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 Points

• The 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.

Links

• Connect with Hendrik on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 6: Bridging the Chasm Between Data Science and Engineering first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 5: Identifying Data Science Use Cases for your Business

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 Bio

Rob 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 Points

• Processes 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.

Links

• Connect with Rob on LinkedIn
• Bird App XKCD Comic

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 5: Identifying Data Science Use Cases for your Business first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 4: The Role of the Board in Maximising the Value of Data

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 Bio

Dr 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 Points

• The 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.

Links

• Connect with Stuart on LinkedIn
• Stuart’s University of Melbourne Profile
• Business Model Transformation – the AI and Cloud Technology Revolution Microsite

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 4: The Role of the Board in Maximising the Value of Data first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 3: Fairness and Anti-Discrimination in Machine Learning

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 Bio

Dr 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 Points

• Direct 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.

Links

• Connect with Fei on LinkedIn
• Fei’s UNSW Public Profile
• Australia’s Voluntary Ethical AI Framework

Fei’s papers on fairML and insurance pricing:

• Anti-Discrimination Insurance Pricing: Regulations, Fairness Criteria and Modelling
• The Discriminating (Pricing) Actuary
• Welfare Implications of Fairness and Accountability for Insurance Pricing

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 3: Fairness and Anti-Discrimination in Machine Learning first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 2: Leading a Technical Team – Transitioning from Individual Contributor to Manager and Beyond

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 Bio

Tim 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 Points

• The 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.

Links

• Connect with Tim on LinkedIn
• Connect with Genevieve on LinkedIn

• Download the FREE Data Science Project Discovery Guide

• Genevieve 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.

The post Episode 2: Leading a Technical Team – Transitioning from Individual Contributor to Manager and Beyond first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Genevieve Hayes Consulting Episode 1: Building Data Science Capability in Data-Focussed Teams

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 Bio

Amanda 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 Points

• The 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.

Links

• Connect with Amanda on LinkedIn
• Data Science Applications Microcredential
• Data Analytics Seminar

• Machines Behaving Badly by Toby Walsh
• How Humans Judge Machines by Cesar Hidalgo

• Download the FREE Data Science Project Discovery Guide
• Connect with Genevieve on LinkedIn

• To 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.

The post Episode 1: Building Data Science Capability in Data-Focussed Teams first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.