Value Driven Data Science

Dr Genevieve Hayes

Value Driven Data Science is a masterclass where data professionals learn how to become strategic experts.

Each week, Dr Genevieve Hayes speaks with world-class data practitioners who have mastered strategic positioning, built genuine authority, and transformed their expertise into organisational influence. You'll learn how they create value by helping stakeholders make better decisions and solve real business problems with data - not just by running analyses.

If you're a data professional ready to stop being a technical executor and become a strategic expert, this masterclass is for you.

Play

Open in

23 episodes
weekly
Avg 21 min
English

Episodes 23

Newest Oldest Longest

All20 Unplayed20 In progress0 Played0

Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.

#120
Wednesday · 12 min
Episode 120: [Value Boost] The AI Silent Correctness Problem
AI hallucinations get all the attention. But hallucinations are relatively easy to catch because the output is obviously wrong. The failure mode that should worry data scientists more is when the agent uses facts that are true to draw conclusions that are false, producing outputs that look perfectly fine. This is known as silent correctness. In this Value Boost episode, Jia Huang joins Dr Genevieve Hayes to explore why silent correctness is the most dangerous failure mode in agentic AI systems and what data scientists can do to catch it before it causes serious harm. You'll discover: Why silent correctness is harder to catch than a hallucination [04:17] Why sampling and auditing are non-negotiable in agentic systems [07:09] Four techniques data scientists can use to catch silent failures [09:28] The one safeguard every agentic AI system should have [11:10] Guest Bio Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems. Links Connect with Jia on LinkedIn Follow Jia on Substack Agent Design Pattern Society (ADPS) website Jia's AI agent design position paper Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#119
August 19 · 29 min
Episode 119: Rewiring Your Data Science Thinking for the Agentic AI Era
The shift to agentic AI doesn't make data science skills obsolete. But it does require data scientists to rewire how they think about familiar concepts, such as uncertainty, model evaluation and accountability, in their work. In this episode, Jia Huang joins Dr Genevieve Hayes to explore what that rewiring actually looks like, and why data scientists are better placed than almost any other profession to make it. You'll discover: Why data scientists are better prepared for the agentic AI era than they might think [03:00] How the data scientist's role is shifting from analyst to system designer [06:37] The three types of uncertainty in agentic AI systems [11:58] Why context engineering is the new feature engineering [23:19] Guest Bio Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems. Links Connect with Jia on LinkedIn Follow Jia on Substack Agent Design Pattern Society (ADPS) website Jia's AI agent design position paper Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#118
August 12 · 11 min
Episode 118: [Value Boost] Compounding Your Data Science Authority Beyond Blog Posts
A well-written blog post gets you noticed. But for data scientists who want to build authority that compounds over time, it's just the beginning. Every piece of writing is a potential stepping stone to something bigger - a conference talk, a book deal, or an opportunity you couldn't have anticipated. In this Value Boost episode, Cynthia Dunlop joins Dr Genevieve Hayes to explore how data scientists can convert blog writing into bigger opportunities and what it actually takes to make the leap from blog post to book. You'll discover: How conference organisers actually find their speakers — and why blogging is the answer [02:35] How acquisitions editors scout for authors and why you don't need a huge following [03:39] The low risk way to find out if you're ready to write a book [06:57] How each new opportunity compounds the authority you've already built [08:10] Guest Bio Cynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack. Links Connect with Cynthia on LinkedIn Follow Cynthia on Substack Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#117
August 5 · 25 min
Episode 117: Writing Your Way to Authority as a Data Scientist
For data scientists who want to build authority beyond their organisation, writing is one of the most powerful tools available. But in a world flooded with AI-generated content, simply publishing is no longer enough. The data scientists who stand out are the ones writing things no AI could have written. In this episode, Cynthia Dunlop joins Dr Genevieve Hayes to share practical frameworks for writing blog posts that stand out, build genuine authority and actually get read. You'll discover: Why AI-generated content has made personal experience more valuable than ever [05:10] The three Ps test for finding topics you can write about with genuine authority [08:56] The blog post patterns that work best for demonstrating expertise [11:03] How to use AI to improve your writing without letting it replace your voice [16:33] Guest Bio Cynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack. Links Connect with Cynthia on LinkedIn Follow Cynthia on Substack Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#116
July 29 · 13 min
Episode 116: [Value Boost] What Data Scientists Need to Know Before the AI Free Ride Ends
Right now, AI is cheap. But the companies providing it are losing billions of dollars a year and are expected to go public within the next 12 months. When they do, the price of intelligence is going up - and any organisation that has built its AI future entirely on rented intelligence is going to face a very unpleasant surprise. In this Value Boost episode, Nicholas Kelly joins Dr Genevieve Hayes to explore why organisations shouldn't be building their AI future entirely on frontier models, and what data professionals need to understand to be the strategic voice in the AI infrastructure conversation. You'll discover: 1. Why relying entirely on frontier AI models is a strategic error [02:00] 2. How the economics of AI are about to change [03:34] 3. The case for owning at least some of your AI infrastructure [07:53] 4. What data professionals need to know to become the strategic voice in AI infrastructure decisions [11:02] Guest Bio Nicholas Kelly is the co-founder and Chief AI Architect of Delivering Data Analytics, a consultancy that helps organisations turn data, BI, analytics, and AI into confident decisions people actually act on. He is also the author of Delivering Data Analytics, How to Interpret Data and the recently released The AI-Driven Data Team. Links Nicholas's Website Connect with Nicholas 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
- Transcript
#115
July 22 · 27 min
Episode 115: Evolving Your Data Career for the AI Era
The AI era presents a choice for data professionals: wait to see what happens to your role, or get ahead of it and decide what it becomes. Nicholas Kelly made that choice two years ago, when a client told him ChatGPT could do 50% of what he did. What followed is a masterclass in proactive career evolution. In this episode, Nick joins Dr Genevieve Hayes to explore how data professionals can evolve their skills for the AI era, what that evolution looks like in practice, and why their existing expertise puts them in a stronger position than they might think. You'll discover: 1. How Nick evolved from dashboard consultant to AI architect [02:35] 2. Why the data team's role is staying the same even as everything around it changes [06:43] 3. How AI is enabling data professionals to build software that used to require a development team [12:11] 4. The best first project to build if you want to develop your AI skills [25:08] Guest Bio Nicholas Kelly is the co-founder and Chief AI Architect of Delivering Data Analytics, a consultancy that helps organisations turn data, BI, analytics, and AI into confident decisions people actually act on. He is also the author of Delivering Data Analytics, How to Interpret Data and the recently released The AI-Driven Data Team. Links Nicholas's Website Connect with Nicholas 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
- Transcript
#114
July 15 · 13 min
Episode 114: [Value Boost] The Four Conversations Every Data Scientist Needs to Master
For data scientists, getting a project approved is a sale. It might not feel like one and money might not change hands, but the dynamics are exactly the same. And like any sale, it goes a lot better if you go in with a plan. In this Value Boost episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use the Four Conversations framework to sell their expertise more effectively, whether as independent consultants or as employees within organisations. You'll discover: Why reputation is the most powerful sales tool a data professional has [04:33] How to stop behaving like a vendor when you're trying to sell expertise [05:09] Why the value of your work resides in your stakeholder not in you [09:53] The single question that unlocks what your stakeholder truly values [12:13] Guest Bio Blair Enns is the founder of Win Without Pitching, the leading authority on selling and pricing for expert advisors and practitioners. He is also the author of The Win Without Pitching Manifesto and The Four Conversations: a New Model for Selling Expertise, and is the co-host, with David C. Baker, of the podcast 2Bobs: Conversations on the Art of Creative Entrepreneurship. Links Connect with Blair on LinkedIn Blair'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
- Transcript
#113
July 8 · 26 min
Episode 113: The Experts' AI Manifesto
Building genuine expertise takes years. And in the age of AI, losing it can happen gradually and almost invisibly, through small delegations that each seem reasonable in isolation but add up to something significant over time. In this episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use AI without compromising their hard-earned expertise and reputation. You'll discover: Why delegating to AI is always a trade-off [03:00] The crucial difference between writing to communicate and writing to think [08:40] Why you should orient yourself around the problems you solve [14:28] How to decide which skills are worth protecting and which to let go [17:49] Guest Bio Blair Enns is the founder of Win Without Pitching, the leading authority on selling and pricing for expert advisors and practitioners. He is also the author of The Win Without Pitching Manifesto and The Four Conversations: a New Model for Selling Expertise, and is the co-host, with David C. Baker, of the podcast 2Bobs: Conversations on the Art of Creative Entrepreneurship. Links The Experts' AI Manifesto Connect with Blair on LinkedIn Blair'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
- Transcript
#112
July 1 · 16 min
Episode 112: [Value Boost] Lies, Damned Lies and Stakeholders
AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers that confirmed what decision-makers already wanted to believe. In this Value Boost episode, Derek Gibson joins Dr Genevieve Hayes to explore how data professionals can help their stakeholders become better data sceptics and avoid being duped by misleading data long before it ever reaches an AI. In this episode, you'll discover: 1. The timeless data traps that catch even experienced decision makers [01:56] 2. How to arm your stakeholders with the right questions to push back on data [07:57] 3. Why confirmation bias is the most dangerous data vulnerability in any organisation [09:20] 4. What it means when an analytics team is asked to confirm a decision rather than inform one [13:24] Guest Bio Derek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms. Links Connect with Derek on LinkedIn Derek'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
- Transcript
#111
June 24 · 26 min
Episode 111: Building Your Defences Against AI Misinformation
AI doesn't lie - at least, not intentionally. It just sounds completely confident while filling in the gaps with whatever seems most plausible. And in a world where AI outputs are increasingly being used to inform high-stakes decisions, the ability to spot what's wrong, before it reaches a stakeholder, is becoming one of the most important skills a data professional can have. In this episode, Derek Gibson joins Dr Genevieve Hayes to share practical strategies for identifying unreliable AI outputs and building the defences necessary to keep AI-generated misinformation from reaching your stakeholders. In this episode, you'll discover: Why AI is not a truth tool and what that means for how you use it [03:21] The red flags that signal an AI output shouldn't be trusted [12:21] A simple prompting habit you can develop to reduce AI mistakes [16:13] Why the skill of verifying AI outputs is one you need to build yourself [24:25] Guest Bio Derek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms. Links Connect with Derek on LinkedIn Derek'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
- Transcript
#110
June 17 · 16 min
Episode 110: [Value Boost] Why You Need Less Data Than You Think
In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint. In this Value Boost episode, Douglas Hubbard joins Dr Genevieve Hayes to share practical techniques from How to Measure Anything that data scientists can start using right now to support high-stakes decisions when observations are scarce and every data point counts. In this episode, you'll learn: Why a single observation reveals more than you think [01:58] How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25] The Rule of Five and what it reveals about small sample statistics [12:08] The simplest and most overlooked technique for reducing measurement uncertainty [14:07] Guest Bio Douglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It. Links How to Measure Anything website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#109
June 10 · 29 min
Episode 109: How to Measure Anything and Make Better Decisions
Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them. They are the high-stakes, one-off decisions made under significant uncertainty - and most data scientists have no framework for handling them. In this episode, Douglas Hubbard joins Dr Genevieve Hayes to share how combining techniques from statistics, economics and decision theory can help data scientists tackle the problems that matter most. In this episode, you'll discover: What Applied Information Economics is and how it works in practice [03:17] Why organisations are systematically measuring the wrong things [09:23] How the Lens Model can make expert judgment more reliable than the expert themselves [13:44] How AI can turbocharge the Applied Information Economics approach [21:10] Guest Bio Douglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It. Links How to Measure Anything website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#108
June 3 · 10 min
Episode 108: [Value Boost] How to Use AI Without Losing Your Edge
AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place. In this Value Boost episode, Tim Dietrich joins Dr Genevieve Hayes to explore how to stay on the right side of that line and what mindful AI use actually looks like in practice. In this episode, you'll discover: Why looking for problems to solve with AI is a warning sign [02:05] What happens when you use AI before you have the expertise to direct it [05:51] Why your AI interactions should be conversations rather than one-way requests [06:54] How to use AI to become a better thinker not just a faster worker [08:40] Guest Bio Tim Dietrich is an independent software developer with over 25 years’ experience building business software for organisations ranging from startups to Fortune 50 companies, including Siemens and the Library of Congress. Recently, he has become known for building a virtual team of AI specialists that allows him to operate with the output and breadth of a small firm, while remaining a team of one. Links Connect with Tim on LinkedIn Tim'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
- Transcript
#107
May 27 · 28 min
Episode 107: Building a Virtual Empire of AI Specialists
The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answer, according to Tim Dietrich, isn't to compete with AI but to do something far more interesting with it - in his case, building a virtual team of over 100 AI specialists to dramatically expand what he is able to achieve. In this episode, Tim joins Dr Genevieve Hayes to share the principles and practicalities behind building a virtual AI team, and what data scientists can learn from his experience. In this episode, you'll discover: How Tim went from being the "world's most negative person on AI" to building a virtual team of over 100 specialists [03:08] What a virtual team of AI specialists can do that a human team can't [06:11] How to build your first AI agent and what to delegate to it [14:19] Why the human in the middle is still the most important person on the team [17:11] Guest Bio Tim Dietrich is an independent software developer with over 25 years’ experience building business software for organisations ranging from startups to Fortune 50 companies, including Siemens and the Library of Congress. Recently, he has become known for building a virtual team of AI specialists that allows him to operate with the output and breadth of a small firm, while remaining a team of one. Links Connect with Tim on LinkedIn Tim'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
- Transcript
#106
May 20 · 11 min
Episode 106: [Value Boost] When AI Isn't the Answer
These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't always the right answer. In this Value Boost episode, Santosh Kaveti joins Dr Genevieve Hayes to explore the situations where AI isn't the answer, how to recognise them, and how to have the conversation with stakeholders who are convinced it is. In this episode, you'll discover: The types of problems where AI consistently falls short [01:36] How to recognise when AI is the wrong tool for the job [04:46] Why most AI conversations eventually lead back to data, people and processes [06:25] How to push back on an AI solution without losing stakeholder confidence [09:43] Guest Bio Santosh Kaveti is the CEO and Founder of ProArch, a technology consultancy that helps enterprises operationalise AI securely and at scale. His expertise spans critical infrastructure industries, including power generation, manufacturing and healthcare, where he has seen firsthand how AI can drive business transformation in complex regulatory environments. Links Connect with Santosh on LinkedIn ProArch website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#105
May 13 · 29 min
Episode 105: From AI Idea to Production Reality
Organisations today have no shortage of AI ideas. What they lack is the ability to turn those ideas into production-ready systems that deliver real business value. For data scientists trying to get AI projects off the ground, understanding why that gap exists is as important as the technical work itself. In this episode, Santosh Kaveti joins Dr Genevieve Hayes to share what organisations consistently get wrong when embarking on AI initiatives, and what data scientists can do to help get it right. In this episode, you'll discover: Why organisations with great AI ideas still fail to deploy them [02:16] What history tells us about where the current AI wave is heading [09:48] The real cost of bolting AI onto systems that weren't designed for it [13:42] How to forge the cross-functional partnerships that get AI projects off the ground [22:21] Guest Bio Santosh Kaveti is the CEO and Founder of ProArch, a technology consultancy that helps enterprises operationalise AI securely and at scale. His expertise spans critical infrastructure industries, including power generation, manufacturing and healthcare, where he has seen firsthand how AI can drive business transformation in complex regulatory environments. Links Connect with Santosh on LinkedIn ProArch website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#104
May 6 · 13 min
Episode 104: [Value Boost] The Four Zones of AI Productivity for Data Scientists
AI can get you to 60% of a finished output in minutes. But getting from 60% to 100% - the part where real insight lives - is where human expertise becomes the deciding factor. And the more expertise you bring, the further AI can take you. In this Value Boost episode, Brent Dykes joins Dr Genevieve Hayes to apply his Four Zones of AI Productivity framework to the insight generation process and explore what it means for data professionals who want to position themselves as strategic advisors. In this episode, you'll discover: The Four Zones of AI Productivity and how they apply to insight generation [01:28] Why AI can help you find an insight but can't generate an actionable one [06:39] Why better AI tools will widen the gap between experts and novices [09:46] How to use AI effectively in your insight generation process [11:44] Guest Bio Brent Dykes is the author of Effective Data Storytelling and the founder of AnalyticsHero. He has consulted with some of the world’s most recognised brands, including Microsoft, Sony, Nike and Amazon, and is a regular contributor to Forbes. Links Connect with Brent on LinkedIn Effective Data Storytelling website Forbes article about the Four Zones of AI Productivity Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#103
April 29 · 30 min
Episode 103: The Art of the Actionable Insight
Most data scientists have been in this situation: you spend hours analysing a dataset, return to your stakeholder with your findings, and are met with a polite "that's interesting" - before your work disappears into a drawer, never to be seen again. The problem usually isn't the analysis. It's that interesting observations and genuine insights are not the same thing. In this episode, Brent Dykes joins Dr Genevieve Hayes to share the frameworks behind identifying and communicating insights that actually move organisations to act. In this episode, you'll discover: What makes an insight an insight and why only 5% of findings qualify [03:42] The four dimensions that focus your analysis before you touch the data [11:25] The six criteria for a truly actionable insight [15:06] Why narrative outperforms an executive summary every time [19:14] Guest Bio Brent Dykes is the author of Effective Data Storytelling and the founder of AnalyticsHero. He has consulted with some of the world’s most recognised brands, including Microsoft, Sony, Nike and Amazon, and is a regular contributor to Forbes. Links Connect with Brent on LinkedIn Effective Data Storytelling website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#102
April 22 · 12 min
Episode 102: [Value Boost] How Giving Away Your Work for Free Can Build Your Authority as a Data Scientist
Building authority as a data professional doesn't require a large budget, a publisher, or even a large audience. But it does require a deliberate decision to share your thinking with the world and the patience to let that compound over time. In this Value Boost episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to share how selectively giving away his work for free helped him become one of the most cited and influential statisticians in the world, and what data professionals at any stage of their career can learn from that approach. In this episode, you'll discover: Why Rob decided to give away his work for free from the start of his career [01:42] How open source software multiplied the impact of his research [05:58] Why authority building is a virtuous cycle and how to start it [09:47] Why starting small is the right move [10:35] Guest Bio Prof. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia. Links Rob's website Otexts' website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript
#101
April 15 · 28 min
Episode 101: Why Traditional Statistics Still Matters in the Age of AI
Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving some of the most powerful analytical tools sitting on the shelf. Tools that handle something modern AI largely can't: uncertainty. In this episode, Prof. Rob Hyndman joins Dr. Genevieve Hayes to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it. In this episode, you'll discover: Why throwing data at an LLM is no substitute for building a model that understands the problem [04:27] How combining classical statistics and machine learning can produce better forecasting results than either approach alone [08:22] What data scientists lose when they stop thinking probabilistically - and why it matters for decision making [12:38] Where to start if you want to strengthen your statistical foundations [25:10] Guest Bio Prof. Rob Hyndman is one of the world’s most influential applied statisticians and a Professor in the Department of Econometrics and Business Statistics at Monash University. He has maintained an active statistical consulting practice for over 40 years, published over 200 research papers, co-authored more than 65 R packages and written five books on time series forecasting. He is also a Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia. Links Rob's website Otexts' website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
- Transcript

Showing 1–20 of 23 episodes