The journey to becoming a “real” data analyst — Analytics is like the writing profession: the basics are easy to get started with and it’s an art, so there are few barriers to entry and anyone can call themselves a “writer.” There’s no guarantee of quality in the title. However, being literate enough to write some tweets hardly makes…

…and why does your machine learning project need both? — Have you been using your loss function for evaluating your machine learning system’s performance? That's a mistake, but don’t worry, you’re not alone. It’s a widespread misunderstanding that may have something to do with software defaults, college course format, and decision-maker absenteeism in AI. In this article, I’ll explain why…

Introducing residuals and the mean squared error (MSE) — The mean squared error (MSE) is one of many metrics you could use to measure your model’s performance. If you take a machine learning class, chances are you’ll come across it very early in the syllabus — it’s usually baby’s first loss function for continuous data. That’s a lot of…

A beginner-friendly primer on two fundamental data science concepts — What is bias? Depends where you’re hearing the word. I’ve made a tongue-in-cheek laundry list of various bias usages for your amusement, but in this article, we’ll focus on one specific species of bias — statistical bias —which I’ll explain in a moment. Target practice Grab your bow and arrows! Or darts. Or cat-cannon. If…

Career advice to guide data analysts to the top — In Data Science’s Most Misunderstood Hero, I described the excellences of each of the three areas in data science. An analyst’s excellence is speed. Don’t misinterpret this to mean that analysts are good at running around…

The journey to becoming a “real” data analyst — When I began speaking at conferences about the importance of analytics, I found that convincing an audience of its value was the easy part. The mood changed when I explained the catch: Analytics is an investment of time… and, as with all investments, there’s a chance you get nothing in…

The journey to becoming a “real” data analyst — Let’s start with a quick summary of three common misconceptions about analytics: Analytics is statistics. (No.) Analytics is data journalism / marketing / storytelling. (No.) Analytics is decision-making. (No!) Misconception #1: Analytics versus statistics While the tools and equations they use are similar, analysts and statisticians are trained to do very different jobs (learn more…

The journey to becoming a “real” data analyst — There are some big differences between an amateur and a professional analyst. We covered the first 6 of 10 in detail in my previous articles [1] [2] and now we’ll dive into a huge one: realistic expectations. Data pro vs amateur difference #7 — Realistic expectations of data If you’re a professional analyst, you know that data doesn’t owe you anything…

The journey to becoming a “real” data analyst — At the start of this blog series, I set out to prove to you that you’re already a data analyst. Yes, you! If you’re reading this, you’re doing analytics right at this very moment. I felt it was important to start there not only for the inspirational message — you…