"Most Read" Data Science Articles - Q3 2014


"Most Read" Data Science Articles - Q3 2014

  • A Day in the Life of a Functional Data Scientist
    Richard Minerich explains how ideas and tools from functional programming can save time, prevent subtle mistakes in data science, and how he incorporates them into his everyday workflow......

  • New to Machine Learning? Avoid these three mistakes
    Modern machine learning (i.e. not the theoretical statistical learning that emerged in the 70s) is very much an evolving field and despite its many successes we are still learning what exactly can ML do for data practitioners. I gave a talk on this topic earlier this fall at Northwestern University and I wanted to share these cautionary tales with a wider audience...

  • Aspiring Data Scientist? Here Are Some At Work Project Ideas
    Do you find yourself wanting to move into Data Science but keep hearing "get some data, analyze it, and you'll be fine..."? Have you developed many of the base skills for data science, such as programming, data analysis, and/or visualization but are unsure of how to apply them? Are you looking to differentiate yourself from the ever-growing pile of aspiring "data scientist" who have taken the usual Coursera classes and done Kaggle competitions? You are not alone...

  • Understanding Random Forests: From Theory to Practice
    The goal of this thesis is to provide an in-depth analysis of random forests, consistently calling into question each and every part of the algorithm, in order to shed new light on its learning capabilities, inner workings and interpretability...

  • Frequentism and Bayesianism: What's the Big Deal?
    Statistical analysis comes in two main flavors: frequentist and Bayesian. The subtle differences between the two can lead to widely divergent approaches to common data analysis tasks. After a brief discussion of the philosophical distinctions between the views, I’ll utilize well-known Python libraries to demonstrate how this philosophy affects practical approaches to several common analysis tasks...

  • How to Use a Decision Tree to Trade Bank of America Stock
    In our last article we went through a basic example of building a machine-learning algorithm to predict the direction of Apple stock, now we’ll explore how you can actually use these algorithms to help you come up with your own strategy...

  • How do I become a data scientist? An evaluation of 3 alternatives"
    One of the most frequent questions we hear, right behind “so, what exactly is a data scientist” or “what makes a great data scientist”, is “how do I become one? I should probably just get a Master’s, right?” Perhaps not anymore...

  • The Top 5 Questions A Data Scientist Should Ask During a Job Interview
    The data science job market is hot and an incredible number of companies, large and small are advertising a desperate need for talent. Before jumping on the first 6-figure offer you get, it would be wise to ask the penetrating questions below to make sure that the seemingly golden opportunity in front of you isn’t actually pyrite.....

  • Supervised Machine Learning: A Review of Classification Techniques
    This paper describes various supervised machine learning classification techniques. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored...

  • Y Combinator Data Science Start-ups
    There are new and exciting commercial opportunities in the data science space. We take a look at the data science start-ups from the latest yCombinator batch...

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