Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
Dr Genevieve Hayes
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Top 10 Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. Episodes
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Episode 20: Using Data Science to Live Better for Longer
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
08/02/23 • 60 min
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
Episode 50: Addressing the Unknown Unknowns in Data-Driven Decision Making
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
11/20/24 • 46 min
When it comes to awareness and understanding, what we know and don’t know can be split into four categories: known knowns; unknown knowns; known unknowns; and unknown unknowns. And to quote former US Secretary of Defence Donald Rumsfeld: “If one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”
When Rumsfeld made his famous “unknown unknowns” speech, he was referring to military intelligence. But the concept of “unknown unknowns” is just as relevant to data and data science. Those data dark spots, or data gaps, can be a real issue when it comes to data-driven decision making.
In this episode, Matt O'Mara joins Dr Genevieve Hayes to discuss the challenges and risks data gaps present to businesses and the community, and what data scientists can do to help address this issue.
Guest Bio
Matt O'Mara is the Managing Director of information and insights company Analysis Paralysis and is the founder and Director of i3, which helps organisations use an information lens to realise significant value, increase productivity and achieve business outcomes. He is also an international speaker, facilitator and strategist and is the first and only New Zealander to attain Records and Information Management Practitioners Alliance (RIMPA) Global certified Fellow status.
Highlights
- (00:55) Understanding information gaps
- (02:33) Matt O'Mara's journey and insights
- (04:58) Real-world examples of information gaps
- (07:30) The impact of information gaps on society
- (11:54) Organizational challenges and solutions
- (25:55) Critical information sources and management
- (31:33) Developing an information lens
- (42:47) The role of data scientists in addressing information gaps
- (45:29) Conclusion and contact information
Links
- i3 website
- Connect with Matt 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
Episode 44: Designing Data Products People Actually Want to Use
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
08/28/24 • 49 min
As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.
But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?
In this episode, Brian T O’Neill joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to create ML and AI products your stakeholders will love.
Guest Bio
Brian T O’Neill is the Founder and Principal of Designing for Analytics, an independent data product UI/UX design consultancy that helps data leaders turn ML & analytics into usable, valuable data products. He also advises on product and UI/UX design for startup founders in MIT’s Sandbox Innovation Fund; hosts the podcast Experiencing Data; founded The Data Product Leadership Community and maintains a career as a professional percussionist performing in Boston and internationally.
Highlights
- Introducing Brian T. O’Neill (00:19)
- Brian’s journey from music to data product design (02:16)
- Understanding the real needs of stakeholders (06:45)
- The importance of user-centered design in data products (09:33)
- Gaining insights through direct user interaction (12:16)
- Focusing on business and user experience outcomes (17:48)
- Debunking the myths of self-serve analytics and dashboarding (22:46)
- Data platforms vs. data products (27:26)
- Defining a data product: the value exchange principle (29:08)
- Designing human-centered data products (32:56)
- The CED framework: conclusions, evidence, data (36:01)
- Final advice for data scientists (45:06)
Links
- Brian’s mailing list
- Designing for Analytics
- Data Product Leadership Community
- CED framework
- Connect with Genevieve on LinkedIn
- Be among the first to hear about the release of each new podcast episode by signing up HERE
Episode 33: Making the Shift from Data Scientist to Datapreneur
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
03/20/24 • 47 min
Data science is among the most in-demand skills of the 21st century, with opportunities existing for data scientists to make a difference and earn good money as an employee in a range of industries. Yet there has also never been a better time to be a data science entrepreneur (or datapreneur).
But for data scientists who have never experienced the entrepreneurial life and who are used to the security of a steady pay check, making the transition from employee to entrepreneur may seem like an impossible leap, regardless of how desirable it may seem.
In this episode, David Shriner-Cahn joins Dr Genevieve Hayes to discuss how data scientists can escape the corporate world and make the transition from employee to datapreneur.
Guest Bio
David Shriner-Cahn is the podcast host and community builder behind Smashing the Plateau, an online platform offering resources, accountability, and camaraderie to high-performing professionals who are making the leap from the corporate career track to entrepreneurial business ownership.
Talking Points
- How entrepreneurship differs from being a regular employee, particularly with regard to mindset.
- The advantages and disadvantages of each way of making a living.
- Making the transition from employment to entrepreneurship and how to gauge if entrepreneurship is right for you.
- Building your network as an entrepreneur.
- How taking a sabbatical can help ease the transition between being an employee and an entrepreneur.
- The value of community.
Links
- Smashing the Plateau
- Connect with Genevieve on LinkedIn
- Value Driven Data Science has recently featured in Feedspot’s list of the 4 Best Australian Data Science Podcasts. You can be among the first to hear about the release of each new podcast episode by signing up HERE
Episode 17: How to Avoid an AI Scandal
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
06/14/23 • 53 min
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
Episode 13: Breeding Data Science Unicorns
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
04/19/23 • 44 min
“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
Episode 45: AI-Powered Investment Insights
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
09/11/24 • 45 min
Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.
Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guesswork or not investing at all.
This is where AI can help, because there’s nothing that AI does better than finding patterns in large volumes of data. AI has the potential to democratize access to investment insights.
In this episode, Andrew Einhorn joins Dr Genevieve Hayes to discuss how AI can help ordinary investors find better investment opportunities than they could ever manage on their own.
Guest Bio
Andrew Einhorn is the CEO and co-founder of Levelfields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Before moving into finance, Andrew started his career as an epidemiologist and helped build a pandemic monitoring system for Georgetown Hospital. He also previously co-founded tech company Synoptus, has consulted for NASA and served as an advisor to a $65 billion hedge fund.
Highlights
- (00:06) Meet Andrew Einhorn
- (02:54) Andrew’s journey from public health to data science
- (07:55) The birth of Levelfields
- (19:35) Event-driven investment insights explained
- (26:22) AI and data science behind Levelfields
- (36:36) User experience and customisation
- (41:03) Future developments and final advice
Links
- Levelfields website
- Connect with Genevieve on LinkedIn
- Be among the first to hear about the release of each new podcast episode by signing up HERE
Episode 30: Cause and Effect Data Science
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
02/07/24 • 60 min
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
Episode 28: The Data Science Behind ChatGPT
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
11/29/23 • 53 min
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
Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.
12/18/24 • 41 min
In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.
But in helping everyone else in the business, data professionals can often forget to help themselves.
However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there’s no reason why they can’t also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.
In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively.
Guest Bio
Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems.
Highlights
- (00:05) Meet Barzan Mozafari
- (00:50) The role of AI in data engineering
- (01:36) The birth of Keebo
- (02:34) Challenges in modern data pipelines
- (05:41) How Keebo optimizes data warehousing
- (07:35) AI and ML techniques behind Keebo
- (08:47) Reinforcement learning in practice
- (16:23) Guardrails and safeguards in AI systems
- (26:29) The build vs. buy dilemma
- (36:03) Future trends in data science and AI
- (39:36) Final advice for data scientists
- (40:50) Closing remarks and contact information
Links
- Keebo website
- Connect with Barzan 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
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FAQ
How many episodes does Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. have?
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. currently has 59 episodes available.
What topics does Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. cover?
The podcast is about Podcasts, Technology, Business and Data Science.
What is the most popular episode on Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.?
The episode title 'Episode 37: Data Privacy in the Age of AI' is the most popular.
What is the average episode length on Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.?
The average episode length on Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. is 51 minutes.
How often are episodes of Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. released?
Episodes of Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. are typically released every 14 days.
When was the first episode of Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.?
The first episode of Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. was released on Sep 15, 2022.
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