Data Skeptic

By Kyle PolichScience Technology Mathematics

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2nd
on Mathematics
Favorites
402
Followers
Typical duration
37 min
per episode

Based on Podcast App listening data

Data Skeptic episodes

May 01, 2022 Does Remote Learning Work?
We explore this complex question in two interviews today. First, Kasey Wagoner describes 3 approaches to remote lab sessions and an analysis of which was the most instrumental to students. Second, Tahiya Chowdhury shares insights about the specific features of video-conferencing platforms that are lacking in comparison to in-person learning.
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Thanks to our sponsor!ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.

49 min
April 25, 2022 Covid-19 Impact on Bicycle Usage
In this episode, we speak with Abdullah Kurkcu, a Lead Traffic Modeler. Abdullah joins us to discuss his recent study on the effect of COVID-19 on bicycle usage in the US. He walks us through the data gathering process, data preprocessing, feature engineering, and model building. Abdullah also disclosed his results and key takeaways from the study. Listen to find out more.
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Thanks to our sponsor!Astrato is a modern BI and analytics platform built for the Snowflake Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.

32 min
April 22, 2022 Learning Digital Fabrication Remotely
Today, we are joined by Jennifer Jacobs and Nadya Peek, who discuss their experience in teaching remote classes for a course that is largely hands-on. The discussion was focused on digital fabrication, why it is important, the prospect for the future, the challenges with remote lectures, and everything in between.

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Thanks to our sponsor! https://neptune.ai/

Log, store, query, display, organize, and compare all your model metadata in a single place
34 min
April 18, 2022 Remote Software Development
Today, we are joined by Denae Ford, a Senior Researcher at Microsoft Research and an Affiliate Assistant Professor at the University of Washington. Denae discusses her work around remote work and its culminating impact on workers. She narrowed down her research to how COVID-19 has affected the working system of software engineers and the emerging challenges it brings.

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Thanks to our sponsor!

Weights & Biases : The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management.

38 min
April 11, 2022 Quantum K-Means
In this episode, we interview Jonas Landman, a Postdoc candidate at the University of Edinburg. Jonas discusses his study around quantum learning where he attempted to recreate the conventional k-means clustering algorithm and spectral clustering algorithm using quantum computing.

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40 min
April 04, 2022 K-Means in Practice
K-means is widely used in real-life business problems. In this episode, Mujtaba Anwer, a researcher and Data Scientist walks us through some use cases of k-means. He also spoke extensively on how to prepare your data for clustering, find the best number of clusters to use, and turn the 'abstract' result into real business value. Listen to learn. Click here to access additional show notes on our website! Thanks to our sponsor! ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale.
31 min
March 28, 2022 Fair Hierarchical Clustering
Building a fair machine learning model has become a critical consideration in today's world. In this episode, we speak with Anshuman Chabra, a Ph.D. candidate in Computer Networks. Chhabra joins us to discuss his research on building fair machine learning models and why it is important. Find out how he modeled the problem and the result found.
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Thanks to our sponsor! https://astrato.io

Astrato is a modern BI and analytics platform built for the Snowflake Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.
35 min
March 21, 2022 Matrix Factorization For k-Means
Many people know K-means clustering as a powerful clustering technique but not all listeners will be as familiar with spectral clustering. In today's episode, Sibylle Hess from the Data Mining group at TU Eindhoven joins us to discuss her work around spectral clustering and how its result could potentially cause a massive shift from the conventional neural networks. Listen to learn about her findings.

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Thanks to our sponsor, Weights & Biases
31 min
March 14, 2022 Breathing K-Means
In this episode, we speak with Bernd Fritzke, a proficient financial expert and a Data Science researcher on his recent research - the breathing K-means algorithm. Bernd discussed the perks of the algorithms and what makes it stand out from other K-means variations. He extensively discussed the working principle of the algorithm and the subtle but impactful features that enables it produce top-notch results with low computational resources. Listen to learn about this algorithm.
43 min
March 07, 2022 Power K-Means
In today's episode, Jason, an Assistant Professor of Statistical Science at Duke University talks about his research on K power means. K power means is a newly-developed algorithm by Jason and his team, that aims to solve the problem of local minima in classical K-means, without demanding heavy computational resources. Listen to find out the outcome of Jason's study.

Click here to access additional show notes on our website!

Thanks to our Sponsors:ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale. https://clear.ml

Springboard Springboard offers end-to-end online data career programs that encompass data science, data analytics, data engineering, and machine learning engineering.
33 min

About Data Skeptic

From the publisher's feed

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying…