Data Science at Home
Episodes
Friday Mar 26, 2021
Apache Arrow, Ballista and Big Data in Rust with Andy Grove (Ep. 145)
Friday Mar 26, 2021
Friday Mar 26, 2021
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.
Our Sponsors
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
Friday Mar 19, 2021
Pandas vs Rust (Ep. 144)
Friday Mar 19, 2021
Friday Mar 19, 2021
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.
Our Sponsors
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
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
Saturday Mar 13, 2021
Concurrent is not parallel - Part 2 (Ep. 143)
Saturday Mar 13, 2021
Saturday Mar 13, 2021
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.
Our Sponsors
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
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
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
Wednesday Mar 10, 2021
Concurrent is not parallel - Part 1 (Ep. 142)
Wednesday Mar 10, 2021
Wednesday Mar 10, 2021
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.
Our Sponsors
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
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.
Tuesday Mar 02, 2021
Backend technologies for machine learning in production (Ep. 141)
Tuesday Mar 02, 2021
Tuesday Mar 02, 2021
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.
Our Sponsors
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.