Web Picks (week of 13 June 2016)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • Models make predictions on Olympic medals
    The EURO cup is not the only tournament receiving attention from data scientists, it seem. How many medals will each country win in Rio at this Summer’s Olympic Games? Researchers who derived predictions from two different models anticipate that the USA, China, Russia, and the UK will retain their top positions in the medals ranking, but Brazil and Japan are expected to make the biggest gains.
  • Boosting Sales With Machine Learning
    “In this blog post I’ll explain how we’re making our sales process at Xeneta more effective by training a machine learning algorithm to predict the quality of our leads based upon their company descriptions.”
  • Microsoft Finds Cancer Clues in Search Queries
    Microsoft scientists have demonstrated that by analyzing large samples of search engine queries they may in some cases be able to identify internet users who are suffering from pancreatic cancer, even before they have received a diagnosis of the disease.
  • Five Mistakes Beginners Make When Working With Databases
    Early on, with so many things to quickly master, the database tends to be an after-though in application design (perhaps because it doesn’t make an impact to end user experience). As a result there’s a number of bad practices that tend to get picked up when working with databases, here’s a rundown of just a few.
  • Building a data science portfolio: Storytelling with data
    Data science is fundamentally about communication. You’ll discover some insight in the data, then figure out an effective way to communicate that insight to others, then sell them on the course of action you propose. One of the most critical skills in data science is being able to tell an effective story using data. An effective story can make your insights much more compelling, and help others understand your ideas.
  • The making of a cheatsheet: emoji edition
    “I’ve mentioned this before, but I really love emoji. Another thing I love is data science. So, I decided to marry these two loves in as productive a fashion as possible.”
  • Visualising city similarity
    This blog post explains an alternative way to figure out how similar cities are. After you read it, you will realize why I think Madison and Reykjavik are very similar cities.
  • How to build up a data team
    “Recruiting is one of those things where the Dunning-Kruger effect is the most pronounced: the more you do it, the more you realize how bad you are at it. Every time I look back a year, I realize 10 things I did wrong. Extrapolating this, I know in another year I’ll realize all the stupid mistakes I’m doing right now. Anyway, that being said, here are some things I learned from recruiting.”