Summary In today's episode, I break down why mastering concepts matters far more than memorizing tools in marketing data science. Here's what this means for you. You'll build transferable analytical skills that survive any software change and let you troubleshoot problems no auto-ML system can catch. You'll also learn these concepts: why correlated variables like…
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…
Jessica asks, “I struggle with forming hypotheses. Do I need more data to get better?” Data probably isn’t the problem. A well-defined question you want the answer to is probably the problem. Consider what a valid hypothesis is, within the domain of marketing data science: a testable, verifiably true or false statement about a single…
Summary In today's episode, I break down why struggling with hypothesis formation is rarely about having too little data and almost always about skipping the foundational steps of the scientific method. Here's what this means for you. You gain a clear framework for crafting testable, single-condition hypotheses that produce reliable, actionable marketing insights. You'll also…
Jessica asks, “How will a data scientist create my model or hypothesis if they don’t know my business?” This is an excellent question. The short answer is: they can’t, not reliably. Not something you’d want to bet your business on. Data science is the combination of four things: business skills/domain knowledge, scientific skills, technical skills,…
Summary In today's episode, I tackle Jessica's question about how data scientists create models when they don't know your business. Here's what this means for you. You discover that domain expertise is one of four essential pillars of data science, and skipping it guarantees wasted time, wasted money, and surface-level results. You'll also learn these…
Jessica asks, “As a Data Scientist for marketing, how do you decide which variables are important?” Generally speaking, feature selection or variable/predictor importance is the technique you’d use to make that determination – with the understanding that what you’ll likely get is correlative in nature. You then have to use the scientific method to prove…
Summary In today's episode, I walk through how to decide which variables matter most when analyzing marketing data, covering everything from regression basics to spotting spurious correlations. Here's what this means for you. You gain a practical framework for separating meaningful business signals from statistical noise that could send your decisions in the wrong direction.…
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…
