"Most Read" Data Science Articles in 2014

"Most Read" Data Science Articles in 2014

  • A non-comprehensive list of awesome things other people did in 2014
    Last year I made a list off the top of my head of awesome things other people did. I loved doing it so much that I'm doing it again for 2014...

  • The Current State of Machine Intelligence
    I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find — my current list has 2,529 of them to be exact. Yes, I should find better things to do with my evenings and weekends but until then...

  • The Things I Wish I Knew - Lessons Learned from Making Data Product
    Talk from DJ Patil, Greylock Partners as part of this seminar series featuring dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field

  • A Data Analyst's Blog Is Transforming How New Yorkers See Their City
    It may have been the fire hydrants that certified Ben Wellington as the king of New York's "open data" movement. Earlier this year Wellington pored over New York City's parking ticket data and identified two hydrants on consecutive blocks that were generating $55,000 a year in tickets, all from cars that appeared to be parking legally...

  • Starting data analysis/wrangling with R: Things I wish I'd been told
    R is a very powerful open source environment for data analysis, statistics and graphing, with thousands of packages available. After my previous blog post about likert-scales and metadata in R, a few of my colleagues mentioned that they were learning R through a Coursera course on data analysis. I have been working quite intensively with R for the last half year, and thought I'd try to document and share a few tricks, and things I wish I'd have known when I started out...

  • What statistical analysis should I use?
    The following table shows general guidelines for choosing a statistical analysis...

  • On Starting a New Job
    I am starting a new job in November. This is not a prank like last time. But before the grand reveal of where, first I’ll subject you to a lengthy blog post about my thoughts about the how and why. Hopefully this provides an additional perspective to the excellent posts by Lana Yarosh and Jason Yip on their experiences on the computer/information science academic job market. But those of you who know the rhythms of the academic job market are already realizing that (spoiler alert), I’m not starting a tenure-track faculty role. Instead, I’m going to spend the next few years being a data scientist...

  • Bayes Rule in an animated gif
    Say Pr(A)=5% is the prevalence of a disease (% of red dots on top fig). Each individual is given a test with accuracy Pr(B|A)=Pr(no B| no A) = 90% . The O in the middle turns into an X when the test fails. The rate of Xs is 1-Pr(B|A). We want to know the probability of having the disease if you tested positive: Pr(A|B). Many find it counterintuitive that this probability is much lower than 90%; this animated gif is meant to help...

  • This Math Model Is Predicting the Ebola Outbreak with Incredible Accuracy
    Part of the allure of epidemiology is being able to describe and predict highly dynamic outbreaks with simple, clean mathematical models. But how close can models really get to perfectly mapping the spread of disease?...

  • How To Choose A Data Science Project For Your Data Science Portfolio
    You want to create a data science portfolio to showcase you can “do” data science. That you know how to take in a data set, clean it up, use various techniques to extract useful information from it, and then communicate the results. The problem is that you aren’t sure where to start, what projects to do, what languages to use, or even what techniques to use...

If you're interested in reading the rest of our "most read" articles this year (i.e., from other quarters), you can check them out here:

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