- Unleash a faster Python on your data - Dec 7, 2017.
Get real performance results and download the free Intel® Distribution for Python that includes everything you need for blazing-fast computing, analytics, machine learning, and more. Use Intel Python with existing code, and you’re all set for a significant performance boost.
- Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization - Nov 29, 2017.
Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code?
- The Guerrilla Guide to Machine Learning with Julia - Jul 12, 2017.
This post is a lean look at learning machine learning with Julia. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
- Introducing Dask for Parallel Programming: An Interview with Project Lead Developer - Sep 7, 2016.
Introducing Dask, a flexible parallel computing library for analytics. Learn more about this project built with interactive data science in mind in an interview with its lead developer.
- Interesting Things I Learned at SciPy 2016 - Jul 21, 2016.
Learn about some interesting projects featured at SciPy 2016, brought to you by an attendee who put in the work to bring you this great list of projects.
- 10 Great Talks From SciPy 2016 - Jul 20, 2016.
Here's a curated short list of interesting and insightful talks to watch from SciPy 2016 to help guide your search through the volume of great video material emerging from the conference.
- An Introduction to Scientific Python (and a Bit of the Maths Behind It) – Matplotlib - Jun 9, 2016.
An introductory overview of Matplotlib, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
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- An Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy - Jun 1, 2016.
An introductory overview of NumPy, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
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