# Tag: SciPy (14)

**Comprehensive Guide to the Normal Distribution**- Jan 18, 2021.

Drop in for some tips on how this fundamental statistics concept can improve your data science.**Sparse Matrix Representation in Python**- May 19, 2020.

Leveraging sparse matrix representations for your data when appropriate can spare you memory storage. Have a look at the reasons why, see how to create sparse matrices in Python using Scipy, and compare the memory requirements for standard and sparse representations of the same data.**Top KDnuggets tweets, Feb 05-11: #SciPy 1.0: fundamental algorithms for scientific computing in #Python; Why is Data Science so popular?**- Feb 12, 2020.

Why is Data Science so Popular?; Visual Paper Summary: ALBERT (A Lite BERT); Uber Has Assembled One of the Most Impressive Open Source DL Stacks; Top #AI Influencers To Follow in 2020**KDnuggets™ News 19:n27, Jul 24: Bayesian deep learning and near-term quantum computers; DeepMind’s CASP13 Protein Folding Upset Summary**- Jul 24, 2019.

This week on KDnuggets: Learn how DeepMind dominated the last CASP competition for advancing protein folding models; Bayesian deep learning and near-term quantum computers: A cautionary tale in quantum machine learning; The Evolution of a ggplot; Adapters: A Compact and Extensible Transfer Learning Method for NLP; 12 Things I Learned During My First Year as a Machine Learning Engineer; Things I Learned From the SciPy 2019 Lightning Talks; and much more!**Things I Learned From the SciPy 2019 Lightning Talks**- Jul 22, 2019.

This post summarizes the interesting aspects of the Day One of the SciPy 2019 lightning talks, a flash round of a dozen ~3 minute talks covering a wide variety of topics.**Unleash a faster Python on your data**- Apr 18, 2019.

Intel’s optimized Python packages deliver quick repeatable results compared to standard Python packages. Intel offers optimized Scikit-learn, Numpy, and SciPy to help data scientists get rapid results on their Intel® hardware. Download now.**Python Data Science for Beginners**- Feb 20, 2019.

Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.**Notes on Feature Preprocessing: The What, the Why, and the How**- Oct 26, 2018.

This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.**Top 20 Python Libraries for Data Science in 2018**- Jun 27, 2018.

Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.**Comparing Distance Measurements with Python and SciPy**- Aug 15, 2017.

This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.**2 must-have tools for blazing fast Python performance**- Sep 15, 2016.

Intel has two must-have, highly optimized tools to help you get faster performance out of the box - with the least amount of effort.**KDnuggets™ News 16:n27, Jul 27: 5 Big Data Projects You Cant Overlook; Pokemon Go and Big Data; SAS vs R vs Python**- Jul 27, 2016.

5 Big Data Projects You Can No Longer Overlook; What Has Pokemon Got To Do With Big Data? 10 Great Talks From SciPy 2016; SAS vs R vs Python: Which Tool Do Analytics Pros Prefer?**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.