Data Science at Home

Latest Episodes:

Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145) Mar 26, 2021

Show notes

Do you want to know the latest in big data analytics frameworks? Have you ever heard of Apache Arrow? Rust? Ballista? In this episode I speak with Andy Grove one of the main authors of Apache Arrow and Ballista compute engine.
Andy explains some challenges while he was designing the Arrow and Ballista memory models and he describes some amazing solutions.

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This episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.
To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascience

If building software is your passion, you’ll love ThoughtWorks Technology Podcast. It’s a podcast for techies by techies. Their team of experienced technologists take a deep dive into a tech topic that’s piqued their interest — it could be how machine learning is being used in astrophysics or maybe how to succeed at continuous delivery.

References

https://arrow.apache.org/

https://ballistacompute.org/

https://github.com/ballista-compute/ballista

Pandas vs Rust (Ep. 144) Mar 19, 2021

Show notes

Pandas is the de-facto standard for data loading and manipulation. Python is the de-facto programming language for such operations. Rust is the underdog. Or is it?
In this episode I am showing you why that is no longer the case.

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Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

Useful Links

https://github.com/haixuanTao/Data-Manipulation-Rust-Pandas

https://github.com/ritchie46/polars

https://github.com/rust-ndarray/ndarray

Concurrent is not parallel - Part 2 (Ep. 143) Mar 13, 2021

Show notes

In plain English, concurrent and parallel are synonyms. Not for a CPU. And definitely not for programmers. In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

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Useful Links

http://web.mit.edu/6.005/www/fa14/classes/17-concurrency/

https://doc.rust-lang.org/book/ch16-00-concurrency.html

https://urban-institute.medium.com/using-multiprocessing-to-make-python-code-faster-23ea5ef996ba

Concurrent is not parallel - Part 1 (Ep. 142) Mar 10, 2021

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Backend technologies for machine learning in production (Ep. 141) Mar 02, 2021

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This is one of the most dynamic and fascinating topics: API technologies for machine learning.

It's always fun to build ML models. But how about serving them in the real world? In this episode I speak about three must-know technologies to place your model behind an API.

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You are the product (Ep. 140) Feb 22, 2021

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In this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not. Our Sponsors

Links

How to reinvent banking and finance with data and technology (Ep. 139) Feb 15, 2021

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The financial system is changing. It is becoming more efficient and integrated with many more services making our life more... digital. Is the old banking system doomed to fail? Or will it just be disrupted by the smaller players of the fintech industry?
In this episode we answer some of these fundamental questions with Alessandro E. Hatami from Pacemakers

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What's up with WhatsApp? (Ep. 138) Feb 07, 2021

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Have you clicked the button? Accepted the new terms?

It's time we have a talk.

Is Rust flexible enough for a flexible data model? (Ep. 137) Feb 01, 2021

Show notes

In this podcast I get inspired by Paul Done's presentation about The Six Principles for Building Robust Yet Flexible Shared Data Applications, and show how powerful of a language Rust is while still maintaining the flexibility of less strict languages.

Our Sponsor

This episode is supported by Chapman’s Schmid College of Science and Technology, where master's and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.
To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascience