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Book Review: Why – A Guide to Finding and Using Causes

A new book, “ Why: A Guide to Finding and Using Causes ,” by Stevens Institute of Technology assistant professor of computer science Samantha Kleinberg is a necessary addition to any data scientist’s bookshelf as it helps bring focus to the dreaded “correlation does not imply causation” conundrum that affects our understanding of data-centric problems.

Data Science and Statistics: Different Worlds?

The video presentation below, courtesy of the Royal Statistical Society, includes a panel of distinguished practitioners to bring their own perspectives on important issues surrounding the growing field of data science.

Coursera Announces First MOOC-Based Master’s Degree in Data Science

Coursera, a leading online education company known for massive open online courses (MOOCs), today announced a professional data science master’s degree from the University of Illinois at Urbana-Champaign.

Becoming a Data Scientist

Here is a compelling interview with data scientist Will Kurt, courtesy of the Becoming a Data Scientist Podcast series. Kurt talks about his path from English & Literature and Library & Information Science degrees to becoming the Lead Data Scientist at KISSmetrics.

Introduction to GPU Computing

As the use of GPUs continues to rise in fields like deep learning, we thought it would be useful to readers not yet familiar with this technology to offer the “Introduction to GPU Computing” presentation below.

Architecting Predictive Algorithms for Machine Learning

In the presentation below, Seth Juarez of DevExpress discusses architecting predictive algorithms for machine learning.

Using Excel Versus Using R

Trying to show the data analysis package R is no more scary than Excel, John Mount of the Win-Vector blog shows a simple analysis both in Excel and in R.

Data Science 101: Recursive Deep Learning

In the talk below, Recursive Deep Learning for Modeling Compositional and Grounded Meaning, Richard Socher, Founder, MetaMind describes deep learning algorithms that learn representations for language that are useful for solving a variety of complex language tasks.

Statistics for Hackers

In this slide deck presentation below, Jake VanderPlas, discusses how you can use your coding skills to “hack statistics” – to replace some of the theory and jargon with intuitive computational approaches such as sampling, shuffling, cross-validation, and Bayesian methods.

Data Science 101: Deep Learning – Theory and Applications

Deep Learning is a hot topic in statistical learning and many data scientists are seeking a place to start. Here is a presentation from the July 23rd SF Machine Learning Meetup at the Workday Inc. San Francisco office. The featured speaker is Ilya Sutskever.