Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!
A model's ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results.
Read a data-driven discussion on the plight of internally displaced persons (IDPs) in Nigeria, and see the real power of data science and data visualization.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.
How to Become a Data Scientist – Part 1
| 