Summary In today's episode, I break down how to design interview questions that reveal whether a data science candidate has real experience or just crash-course knowledge. Here's what this means for you. You'll learn to spot the difference between a candidate who can recite answers and one who actually understands what will go wrong in…
Summary In today's episode, I break down how the scientific method applies to data science and why the problem definition phase matters far more than most people realize. Here's what this means for you. You'll learn why rushing past exploration and straight to a hypothesis almost always leads to flawed experiments and wasted effort. You'll…
Oz asks, “I have a question about what you mean about data quality can’t be sold and it’s seen as overhead? I suspect we’re talking about 2 different things but I’m curious about what you’re describing.” In the data analytics and data science process, data quality is absolutely foundational – without it, nothing else matters.…
Summary In today's episode, I explore why organizations treat data quality as overhead instead of an investment and what that mindset costs them. Here's what this means for you. You discover how investing in clean data dramatically improves your analysis results and your decision-making accuracy. You'll also learn these concepts: why naively trusting machine-generated data…
Summary In today's episode, I walk through turning the public Open Food Facts nutrition database into advanced, original content marketing using R. Here's what this means for you. You gain a repeatable method for transforming any open dataset into unique, opinion-driven content by cleaning, scaling, and weighting the fields to match your editorial point of…
Summary In today's episode, I break down what it really means to be a citizen data scientist, exploring both interpretations and the hidden risks that come with applying data science skills to causes outside your professional expertise. Here's what this means for you. You gain a practical framework for knowing when your data science contributions…
Summary In today's episode, I walk through analyzing the Johns Hopkins COVID-19 dataset in R to compute velocity and acceleration of case and death counts over time. Here's what this means for you. You gain a reusable derivative-based framework that turns any raw time series into a clear story about whether a trend is speeding…
Jason asks, “Can you describe a marketing initiative that illustrates the intersection of data and content?” Data is an ingredient, and a fairly substantial one, for any form of content marketing. In content marketing, you have an opinion, a point of view, and then you have to support that opinion. That’s where data comes into…
Ashwin asks, “I have data around me related to campaigns – emails, phone calls, and survey data – so how should I start analyzing it?” First, let’s recall that marketing data science is about the application of the scientific method to data. Our first priority, then, is to use the data we have, techniques like…
