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In this week’s episode of the Machine Learning Café podcast, Dr Eugene Dubossarsky spoke with Miklos Toth and Levente Szabados about his experiences in machine learning since the 1980s. Listen to hear Dr Dubossarsky’s opinions on random forests, neural nets, boosting strategies, the importance of understanding data and statistics, and even the effects of the covid-19 crisis.

Dr Eugene Dubossarsky recently spoke with business journalist Leon Gettler on the Talking Business podcast about the importance of data in business today. The discussion focused on how data analytics will increasingly be crucial to good decision-making, and how businesses are being held back by a lack of data literacy, especially among leadership. Listen to the full conversation to hear Dr Dubossarsky’s recommendations on how leaders can improve their data literacy, along with his surprising choice of favourite data-enabled manager.

The second presentation in Dr Eugene Dubossarsky’s three-part series on the future of analytics explores of the role of data literacy as a foundational skill for business conversations. Starting with a discussion of how data scientists must understand what decisions their work supports, and what constitutes a good decision, Dr Dubossarsky argues that data science and analytics has a duty to add real value to organisations and businesses.

Good decision-making is being made ever more important by globalisation, automation, AI, and other drivers of rapid change. It’s one of the few human activities that can’t be automated, though it can be aided by powerful computational tools—such as data analytics.

In an event sponsored by Westpac and AlphaZetta, Dr Eugene Dubossarsky painted a picture of what the future of analytics could look like if analytics were routinely done right. Data literacy would be as universal as computer literacy is today, and “executives” defined and rewarded by their ability to decide well. Organisations would be structured, incentivised and managed following the effective measurement and analysis of the most important metrics.

Watch the full presentation to find out more about what that future could bring.

Dr Eugene Dubossarsky recently appeared as a guest on the Humans of AI podcast, discussing his background in artificial intelligence before deep-diving into some of the problems and potential solutions in the data science industry. He talks about what makes a good data scientist (and what makes a bad one), before wrapping up with his best advice on how to get started in the field.

Listen on Apple Podcasts, Spotify, Player FM, Stitcher, or Podbean.

Dr Eugene Dubossarsky spoke recently at Sydney Future Shapers about the need to foster data literacy not just within the analytics function, but within whole organisations, and particuarly among senior management. His presentation describes why data literacy will soon be an essential part of all professionals’ skill set due to its importance in avoiding business failures and extracting value from data science, analytics, AI and digital transformation.

Dr Eugene Dubossarsky gave an updated version of his iconic presentation, “The Zen of Data Science” at YOW! Data 2018:

What makes data science such a different field? Why is it such a challenge to structure, manage and capture the value of data science? This presentation will focus on the key issues around the practice of discovery, (“science”) and how it differs from the practice of building new things (“engineering”). Other questions addressed will include: How do organisations manage and leverage the value of data science? What are the key “unknown unknowns” that managers miss so often, with disastrous results? What are the cultural, procedural, managerial differences between “scientists” and engineers in a modern, data-driven workplace?

Presciient founder Dr Eugene Dubossarsky recently spoke to Data Science Sydney on how to use data and analytics in strategic decision-making.

Data, analytics methods and metrics are in short supply when it comes to the most important decisions facing an organisation, and senior executive engagement with analytics remains tentative as a result. But what if there was an unambiguous KPI for measuring decision making effectiveness? And what if this method also led to reliably, measurably better decision making? And that identification of good decisions makers (potential or actual) was a side benefit of this method?

Find out more in the full presentation: