I would not have a data science career without Kaggle. So if you are looking for a blog post bashing Kaggle, this is not the place. Competing on Kaggle is worth it! It will teach you a lot about machine learning, give you a project to talk about in interviews, which can even help you get a job! That said, I am not a radical that thinks Kaggle is the ultimate thing that everyone must do in order to become a data scientist....

I have been using daily machine learning development journals for the past 5 years. These are simple diaries that help me organize my thoughts, come up with new ideas and track my progress. I have found them to be an invaluable tool for helping me improve as a data scientist, so I decided to share my experience, as you can find it useful too. 🙂 What is a Daily Machine Learning Journal?...

The days when you needed to hire a full-time data scientist to use machine learning to solve a business problem in your company are long gone. Now, machine learning is more accessible than ever and there are several ways you can start using it in your business without breaking the bank. Here are a few steps you should take to build your first ML-powered solution. Identify A Measurable Business Problem Start by identifying a business problem that machine learning could help solve....

I once heard a marketer teach the following strategy: Create a Video Views campaign and have people watch 95% of a long video for pennies on the dollar. Retarget this audience with conversion ads selling a product. Collect the 💰💰💰 I tried it and it looks like magic! Thousands of people “watch” a long video about your product or niche. But even though I am a big fan of Meta Ads, it sounded too good to be true…...

Last time, I learned it’s very important to have the most granular data possible to build a modern marketing mix model. Usually, this means having daily impressions and conversions (e.g. sales) data. I wanted to find a way to use the power of daily models even with products that don’t sell every day. My course sales data have more days without sales than days with sales, which makes it into a zero vs something prediction instead of a regular regression....

I found about 7 hours of videos of 3 Meta Summits on Marketing Mix Modeling recorded in 2021. They invited experts and companies that used marketing mix models to improve their ROI on the platform. I watched all of it, and here are my learnings. Granularity Is King We need to divide and conquer our datasets. Most of the presentations shared the importance of looking at the data deeper than only at the channel level....

“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” John Wanamaker (1838 - 1922) Knowing where to spend an advertising budget has always been a big problem for advertisers. The gold standard is testing, which every respectable advertiser does often. It goes back to the 1920s when Claude Hopkins published the still relevant “Scientific Advertising” talking about “measuring keyed returns”....

During a technical meeting a few weeks ago, we had a discussion about feature interactions, and how far we have to go with them so that we can capture possible relationships with our targets. Should we create (and select) arithmetic interactions between our features? This is not something we usually learn in machine learning courses, but it’s a very important topic to better understand how our models work. A few years ago I remember visiting a website that showed how different models approximated these simple operations....

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MOOCs are free online courses offered by renowned universities. Anyone can do it, and there are hundreds of them available in the most diverse areas of study. On this page you can find the MOOCs I completed, organized by area of interest and platform. Coursera.org Machine Learning/Data Science Computing for Data Analysis, Johns Hopkins University, Prof. Roger Peng Mathematical Biostatistics Bootcamp 1, Johns Hopkins University, Prof. Brian Caffo Introduction to Data Science, University of Washington, Prof....