Jessica asks, “what should be the interview questions when hiring a good data scientist?” The answer to this question depends heavily on how fluent you are in the language of data science, in order to sniff out unqualified candidates. Focus a lot on scenarios, and work with a non-competitive data scientist to build out questions…
Jessica asks, “What is most common scientific method to analyze data, so when I (business person) is working with marketing data scientists I can have a intelligent conversation?” To my knowledge, there is only one scientific method. What matters for marketing data science (and data science in general) is the implementation – particularly at the…
Jessica asks, “Which should we focus on learning most in marketing data science, concepts or tools?” Without a doubt, concepts. You learn frying, not a specific model of frying pan. You learn painting, not a particular paint brush. You learn to play any piano, not just one kind of piano. Can’t see anything? Watch it…
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.…
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,…
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…
Jessica asks, “How do you feel about citizen data scientists?” I love the theory, the concept, and to be sure, there are plenty of people who are data scientists that lend their expertise to causes and movements outside of their day jobs. But the question is, is a citizen data scientist someone who is a…
