Summary In today's episode, I walk through the best tools for cleaning data and why the right choice depends on your data type, what you're trying to fix, and the scale of the problem. Here's what this means for you. You'll see why human judgment and process matter far more than any specific software tool.…
Summary In today's episode, I walk through a year-over-year analysis of FBI hate crime data comparing 2017 to 2018 and reveal what the numbers actually say versus what they appear to say. Here's what this means for you. You'll see how a metric can look great on paper while the underlying data quality quietly collapses…
Steve asks, “I’m looking for a dataset of companies that are actively selling on Amazon. How would you as a marketing data scientist approach this problem?” That’s an interesting question. To my knowledge, there aren’t publicly available, free datasets of this sort (though please leave a link in the comments if you know one), so…
Summary In today's episode, I walk through a practical methodology for building your own dataset of Amazon sellers when no public source exists. Here's what this means for you. You gain a reproducible framework for identifying e-commerce sellers using web technology signals, saving you from relying on unvetted vendor lists. You'll also learn these concepts:…
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…
