Poetry is great in managing dependencies in the modern pyproject.toml, but it doesn’t provide mono repo features (at least not yet, see issues 2270 and 936). Fortunately, it is quite easy to adapt poetry to manage a mono repo. I’ve made an example available here. A mono repo is a…

For over a decade, marketeers have been using lead scoring to prioritize their engagement activities and drive revenue. Now it’s time to move on, and use the right tool for the job. We’ll show how a prescriptive approach is more effective and will do a much better job. Lead scores The primary…

Getting more and more responsibilities results in different kind of tasks, full agenda’s and less time to focus. As I explained in the Skill Stack, being effective and efficient is a foundational behavioral competency. …

When building Data Science products, an important aspect is getting your data available and ready to use. You’ll need a platform to bring data together and serve it across the company. But how do you go about developing such a data platform? It’s easy to get lost when reading about…

Today I’d like to explain the underlying concepts of Inverse Propensity Weighting (IPW). As a Machine Learning Engineer, it’s important to know how models and algorithms work. This allows you to determine when approaches work and understand why they might not work. You need to really grasp the structure, concepts…

Professionals need to grow and develop their skills to advance in their career. That’s not different for a data scientist. There are various skills, all contributing to your impact on the project. Developing all would be ideal, but makes it hard to focus. …

with uplift modeling, causal inference and Thompson sampling TL;DR;

In the business to consumer market, there are two strategies to grow market share: gaining new customers, and retaining existing customers. The latter challenge is referred to as preventing churn. So churn rate, or even better, retention rate is a key performance indicator. …

Finally, version control for your data. Pachyderm is the Git for your data. It tracks data revisions and associated transformations. It clarifies the data lineage and transformation dependencies. It actually runs the transformations, ensuring all data sets are always up to date. Working towards reproducibility & traceability Reproducibility is an important aspect of data science…