In my last post, I described Big O notation, why it matters, and common search and sort algorithms and their time complexity (essentially… — In my last post, I described Big O notation, why it matters, and common search and sort algorithms and their time complexity (essentially, how fast a given algorithm will run as data size changes). Now, with the basics down, we can begin to discuss data structures, space complexity, and more…

Note: This is part 1 of a 2-part series. For the follow-up post, see here. — Note: This is part 1 of a 2-part series. For the follow-up post, see here. In data science, computer science and statistics converge. As data scientists, we use statistical principles to write code such that we can effectively explore the problem at hand. This necessitates at least a basic understanding…

As data scientists, we are uniquely positioned to assess relationships between variables within a larger context and ultimately, tell… — As data scientists, we are uniquely positioned to assess relationships between variables within a larger context and ultimately, tell stories with statistics. In short, we use hypothesis testing to prove some hypothesis about our data. This is a fairly straightforward process until we want to test a set of hypotheses…

In the tangle of code and relentless momentum of technology, it is often too easy to lose sight of the greater purpose of our work. — In the tangle of code and relentless momentum of technology, it is often too easy to lose sight of the greater purpose of our work. While we all carry different motivations with us, I believe that we are unified by the desire to influence with our work and contribute meaningfully…