Development Workflows for Data Scientists • Free eBook

Development Workflows for Data Scientists - Free eBook.JPGEnabling Fast, Efficient, and Reproducible Results for Data Science • via GitHub

GitHub partnered with O’Reilly Media to examine how data science and analytics teams at several data-driven organizations are improving the way they define, enforce, and automate development workflows.

Download this complimentary book from: – https://resources.github.com/whitepapers/data-science/ 

Power BI custom visual from Visio

Visualize business process workflows, real-world layouts like factory floor plans, network diagrams, organization structures or any illustration created in Microsoft Visio and easily connect it to Power BI data. Contextually represent Power BI data as colours or text on Visio diagrams. Now drive Operational Intelligence effectively using Visio custom visual.

AI for security: Microsoft Security Risk Detection makes debut

Full details at: – https://blogs.microsoft.com/next/2017/07/21/ai-for-security-microsoft-security-risk-detection-makes-debut/

Microsoft is making a cloud service that uses artificial intelligence to track down bugs in software generally available, and it will begin offering a preview version of the tool for Linux users as well.

Microsoft Security Risk Detection, previously known as Project Springfield, is a cloud-based tool that developers can use to look for bugs and other security vulnerabilities in the software they are preparing to release or use. The tool is designed to catch the vulnerabilities before the software goes out the door, saving companies the heartache of having to patch a bug, deal with crashes or respond to an attack after it has been released.

Free course on Deep Learning for Self-Driving Cars

self drive cars.pngA free course and introduction to deep learning through the applied task of building a self-driving car. Taught by Lex Fridman.

Visit http://selfdrivingcars.mit.edu/ for full details of “MIT 6.S094: Deep Learning for Self-Driving Cars“.

Data Science: Performance of Python vs Pandas vs Numpy

Re-post from http://machinelearningexp.com/data-science-performance-of-python-vs-pandas-vs-numpy/

Speed and time is a key factor for any Data Scientist. In business, you do not usually work with toy datasets having thousands of samples. It is more likely that your datasets will contain millions or hundreds of millions samples. Customer orders, web logs, billing events, stock prices – datasets now are huge.

I assume you do not want to spend hours or days, waiting for your data processing to complete. The biggest dataset I worked with so far contained over 30 million of records. When I run my data processing script the first time for this dataset, estimated time to complete was around 4 days! I do not have very powerful machine (Macbook Air with i5 and 4 GB of RAM), but the most I could accept was running the script over one night, not multiple days.

Thanks to some clever tricks, I was able to decrease this running time to a few hours. This post will explain the first step to achieve good data processing performance – choosing right library/framework for your dataset.

The graph below shows result of my experiment (details below), calculated as processing speed measured against processing speed of pure Python.

Python vs Numpy vs Pandas

As you can see, Numpy performance is several times bigger than Pandas performance. I personally love Pandas for simplifying many tedious data science tasks, and I use it wherever I can. But if the expected processing time spans for more than many hours, then, with regret, I change Pandas to Numpy.

I am very aware that the actual performance may vary significantly, depending on a task and type of processing. So please, treat these result as indicative only. There is no single test that can shown “overall” comparison of performance for any set of software tools.

Posted on July 15, 2017 by

see full post @ http://machinelearningexp.com/data-science-performance-of-python-vs-pandas-vs-numpy/