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Articles by Benoit Pimpaud
The Data Analyst Every CEO Wants
Data Analyst is probably the most underrated job in the data industry With only self-service as the last level in the analytics game, we often underestimate how much an ad-hoc analysis made by someone who understands business AND understands data can be valuable. I got the chance to work with a few in my previous experiences. Still, it’s hard to find these unicorns. The following lines list the top 5 things best to learn and focus on for being the analyst that every CEO should have by his side.
The Data Analyst Every CEO Wants
The Data Analyst Every CEO Wants Data Analyst is probably the most underrated job in the data industry. With only self-service as the last level in the analytics game, we often underestimate how much an ad-hoc analysis made by someone who understands business AND understands data can be valuable. I got the chance to work with a few in my previous experiences. Still, it’s hard to find these unicorns.
A Guide to Machine Learning Model Interpretability
Machine learning (ML) has become an essential tool for businesses and researchers to extract insights and predictions from large and complex datasets. However, despite the many benefits of using ML models, they are not always widely adopted in production environments. Projects stay in Jupyter Notebooks, stakeholders understand nothing, or projects are so long and complex they are impossible to integrate into the final product.
Toward Declarative Data Orchestration with Kestra
Orchestration is probably the most important step when dealing with data. It answers the question "How to move data from one point to another, at scale, on time and with observability ?" The way we process data is probably as important, but in the end we still need a way to put the final assets into motion. That's data orchestration. However, we're still early on this part. We have plenty of tools to process data, but not that much to bring harmony and precision to the actual maelstrom.
Data Engineer is a Transitional Job
We often get questions like "What's the difference between a BI engineer and a data engineer" ? "Is the analytics engineer the new BI engineer" ? Those questions are definitely relevant. Beyond buzz words, they catch the same feeling that, data in general, isn't really a new field. That there are new words around, but the real background behind them is the same as ten years ago ... Let's step back 20 years ago. Companies are already using data to take decisions.
How to make great schemas
Tips and tricks to create well-crafted schemas Explaining data assets is hard. There are a lot of tools, technical details, and jargon. All the engineering and scientific systems underneath, though, often respect some kind of architecture—some kind of design. That’s why we create schemas. To explain machine learning models. To share software architectures. To document our pipelines. Creating a schema is an important marker for the adoption of a system.
Data Visualization Before Machine Learning
Do you ever ask yourself why your machine learning model isn’t used? Why do so few people really believe in the power of machine learning rather than these old dashboards? When I was working in a football club, I made a data visualization showing player performances during the season. It was a really simple tile plot. But when football people saw it on my screen they engaged quite quickly.
Why does your Machine Learning Ops feel inadequate?
Every time I design a new microservice, there is a TODO checklist that I check against. An early item in this list is about setting up CI/CD for the new service. I think about what the artifacts are and how they get formed at various stages. I consider how it eventually gets deployed to production and how it gets monitored post-deployment. This situation is not unique to me. Modern software engineering practices implement well-thought CI/CD principles as a norm.
Easy development environments with Operation
Like more and more engineers, data scientists are moving to Docker. This framework sets apart resources and applications from the global system, helps for better reproducibility, and thus take out some headaches during software installation and deployment. While it is mainly used to deploy isolated applications, we can leverage this technology in a slightly derived way to build lightweight and portable development environments.
Beyond the Goal: Visualizing Expected Assists in Soccer
Basketball games have score lines in the triple digits. An NFL game in 2019 might produce a combined double-digit touchdowns. However, in a low-scoring game such as football (not the NFL, rather, what North American fans know as soccer), the final score often does not provide a clear picture of performance. Some of the most exciting and intense games will end with no goals but nevertheless a lot of breathless action and numerous shot attempts.
PassSonar: Visualizing Player Interactions in Soccer Analytics
Democratized thanks to Michael Lewis’ Moneyball (both the book and then the movie adaptation), baseball and basketball have already gone quite far in the realm of sports analytics. Soccer is now also clearly in the place to take the next step, with a lot of work being done by companies like Opta and Statsbomb to explain and create analyses and statistical models.
Phil Foden, the "Stockport Iniesta"
Being a young player at a big club is not easy. It’s even truer at Manchester City where all positions are covered twice (almost three times) and where the bench still contains world-class quality. Almost 10 years on from Mansour’s takeover, not one English player from City’s academy made a Premier League or Champions League appearance last season. Phil Foden is the exception. He has been at Manchester City since the age of nine.
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