Podcast | Understanding Predictive Analytics (O’Reilly)

In this episode of the O’Reilly Data Show, O’Reilly’s online managing editor Jenn Webb speaks with Natalino Busa on the topic of predictive analytics, the challenges of feature engineering, and a new class of techniques that is enabling features to emerge from patterns within the data.

They also discuss the relationship between predictive techniques and high-quality microservices, and how machine learning is being used to improve financial services. Listen to Podcast

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“10 Myths About Machine Learning” – (Prof. Pedro Domingos)

Machine Learning is at the core of data science and we see it’s applications all over now (i.e. recommender engines, etc.). As Pedro Domingos’s Professor of Computer Science U. Washington writes in the piece, “In reality, the main purpose of machine learning is to predict the future.” It’s important to be aware of the MYTHs associated with Machine Learning. Read More

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2016 Data Science Salary Survey and More | (Free eBook)

IN THIS FOURTH EDITION of the O’Reilly Data Science Salary Survey. They analyzed input from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states.

Through the results of their 64-question survey, They’ve explored which tools data scientists, analysts, and engineers use, which tasks they engage in, and of course—how much they make. READ MORE

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How AirBnB Scaled from 5 to 70+ Data Scientists in 2 Years (via Kaggle)

In 2013, Airbnb had a small, centralized team of five data scientists serving the data needs of the company. Since then, they have grown to become one of the largest, most innovative startup teams with over 70 data scientists now serving separate business units. In addition to setting a consistently high bar on new hires and focusing on technical mentorship from peers, the structure of the organization has been key to successful growth. Read More

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LIST | 500+ Data Science Degrees and Certificates from Universities (via Data Science Central)

“This list of 500+ was started in 2012, updated in 2014 and also very recently according to the author. It was compiled by 101.datascience.community, and broken down by degree (master / bachelor / certificate / doctorate) and location (online / on-site.)” – Source Data Science Central Read More

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50+ Open Source Tools for Big Data

Open source software tools have become all the rage, especially around big data and that is a GOOD thing. It allows for many players to work off of the same code base to build more add-on tools and it’s cheap and easy for the masses to get set up and use them. Hadoop, R, Cassandra, Mongo DB, Neo4i and HBase are among the most popular, but there are many more.

I have accumulated 3 lists that are very popular. Please let me know if you see things missing and I’ll attempt to create one large master list and post it on the site. Read More…

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