The Right (big data) Tool for the Job with Jay Shankar
Data Skeptic7 Juli 2014

The Right (big data) Tool for the Job with Jay Shankar

In this week's episode, we discuss applied solutions to big data problem with big data engineer Jay Shankar. The episode explores approaches and design philosophy to solving real world big data business problems, and the exploration of the wide array of tools available.

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Covid-19 Impact on Bicycle Usage

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. Click here for additional show notes on our website. 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.

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Learning Digital Fabrication Remotely

Learning Digital Fabrication Remotely

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22 Apr 202233min

Remote Software Development

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. Click here to access additional show notes on our website! Thanks to our sponsor! Weights & Biases : The developer-first MLOps platform. Build better models faster with experiment tracking, dataset versioning, and model management.

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Quantum K-Means

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. Click here to access additional show notes on our website!

11 Apr 202239min

K-Means in Practice

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.

4 Apr 202230min

Fair Hierarchical Clustering

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. Click here to access additional show notes on our webiste! 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.

28 Mars 202234min

Matrix Factorization For k-Means

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. Visit our website for additional show notes Thanks to our sponsor, Weights & Biases

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