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Using Excel to do precision journalism, an Update from the School of Data Journalism in Perugia. Our first workshop has just kicked off with Steve Doig leading “Excel for Journalists”. If you missed it, don’t worry – here’s the breakdown for you! Download the Data and the Tutorial You can download the full data for this tutorial and a text version of the tutorial itself via Steve’s website. The Tutorial

66 job interview questions for data scientists We are now at 91 questions. We've also added 50 new ones here, and started to provide answers to these questions here. These are mostly open-ended questions, to assess the technical horizontal knowledge of a senior candidate for a rather high level position, e.g. director. Title: The Mystery of the Binary Author: Viznut Originally published in the [ALT] magazine issue 0x0000 The subcultures of computing are very young. There are no legends nor values that come from the distant past. No ancient mysticism, no generations-old symbols that have deep emotional effects.

Data Science Wars: Python vs. R As I frequently travel in data science circles, I’m hearing more and more about a new kind of tech war: Python vs. R. I’ve lived through many tech wars in the past, e.g. Windows vs. Linux, iPhone vs. Scraping websites using the Scraper extension for Chrome If you are using Google Chrome there is a browser extension for scraping web pages. It’s called “Scraper” and it is easy to use. It will help you scrape a website’s content and upload the results to google docs. Walkthrough: Scraping a website with the Scraper extension

Getting Started with Python for Data Scientists With the R Users DC Meetup broadening its topic base to include other statistical programming tools, it seemed only reasonable to write a meta post highlighting some of the best Python tutorials and resources available for data science and statistics. What you don’t know is often the hardest part of picking up a new skill, so hopefully these resources will help make learning Python a little easier. Prepare yourself for code indentation heaven.

Detecting multicollinearity using variance inflation factors Printer-friendly version Okay, now that we know the effects that multicollinearity can have on our regression analyses and subsequent conclusions, how do we tell when it exists? That is, how can we tell if multicollinearity is present in our data? Some of the common methods used for detecting multicollinearity include: Deep Learning - The Biggest Data Science Breakthrough of the Decade Duration: Approximately 60 minutes. Cost: Free Machine learning and AI have appeared on the front page of the New York Times three times in recent memory: 1) When a computer beat the world's #1 chess player 2) When Watson beat the world's best Jeopardy players 3) When deep learning algorithms won a chemo-informatics Kaggle competition. We all know about the first two... but what's that deep learning thing about?