In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data. In this episode you will learn:<ul>
<li>What the Harvard Data Science Review is and why Xiao-Li founded it [5:31]</li>
<li>The difference between data science and statistics [17:56]</li>
<li>The concept of 'data minding' [22:27]</li>
<li>The concept of 'data confession' [30:31]</li>
<li>Why there’s no “free lunch” with data, and the tricky trade-offs that abound [35:20]</li>
<li>The surprising paradoxical downside of having lots of data [43:23]</li>
<li>What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [55:47]</li>
</ul> Additional materials: <a href="https://www.superdatascience.com/581">www.superdatascience.com/581</a>

In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data. In this episode you will learn: What the Harvard Data Science Review is and why Xiao-Li founded it [5:31] The difference between data science and statistics [17:56] The concept of 'data minding' [22:27] The concept of 'data confession' [30:31] Why there’s no “free lunch” with data, and the tricky trade-offs that abound [35:20] The surprising paradoxical downside of having lots of data [43:23] What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [55:47] Additional materials: www.superdatascience.com/581

In this episode founding Editor-in-Chief of the Harvard Data Science Review and Professor of Statistics at Harvard University, Prof. Xiao-Li Meng, joins Jon Krohn to dive into data trade-offs that abound, and shares his view on the paradoxical downside of having lots of data. In this episode you will learn:<ul>
<li>What the Harvard Data Science Review is and why Xiao-Li founded it [5:31]</li>
<li>The difference between data science and statistics [17:56]</li>
<li>The concept of 'data minding' [22:27]</li>
<li>The concept of 'data confession' [30:31]</li>
<li>Why there’s no “free lunch” with data, and the tricky trade-offs that abound [35:20]</li>
<li>The surprising paradoxical downside of having lots of data [43:23]</li>
<li>What the Bayesian, Frequentist, and Fiducial schools of statistics are, and when each of them is most useful in data science [55:47]</li>
</ul> Additional materials: <a href="https://www.superdatascience.com/581" rel="noopener noreferrer">www.superdatascience.com/581</a>

581: Bayesian, Frequentist, and Fiducial Statistics in Data Science

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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Technology

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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581: Bayesian, Frequentist, and Fiducial Statistics in Data Science

581: Bayesian, Frequentist, and Fiducial Statistics in Data Science

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