Moving from Jupyter notebooks to production is not that difficult after all — If you’re a data scientist, you probably spend a lot of time developing intricate Jupyter notebooks to perform data analysis, build complex training pipelines, or compute statistics. Jupyter notebooks are great for this and allow us to prototype ideas in no time. But, what happens once you’re done with this…

Just like unit tests, type hints take developer time but pay off in the long run — Python is a dynamically-typed programming language. This means that the interpreter performs type checking only when the code runs and the variable type is allowed to change over its lifetime. Although Python has always remained dynamically typed and was never intended to change this direction, type hints have been introduced…

I’ve never used spaCy beyond simple named entity recognition tasks. Boy was I wrong. — I’ve recently started to revisit the spaCy library and try out many of its core functionalities to perform low-level linguistic tasks. If you’ve never heard about it, spaCy is a modern natural language processing library that handles +66 languages, provides state-of-the-art speed, and has various components for named entity recognition…

A new widget to better structure the layout of your apps — As a data scientist, I use Streamlit a lot to build interactive web applications and showcase demos and prototypes. Streamlit is extremely easy to use, it has a very intuitive syntax and gets you up to speed for prototyping, in no time. You don’t need to know HTML or JavaScript…

Garbage in, garbage out: good-quality data is the fuel to robust ML engines — I recently had to build an NLP model for a very specific task in a very technical domain. This was challenging since I didn’t know anything about the subject matter and no training data was available. As a data scientist, I first tried researching open-source datasets, pre-trained models, and scientific…

A new scraping tool to generate leads and market insights — What if I told you that you could scrape any data from any website, with a click of a button and without writing a single line of code? Sounds tempting? In this article, I’m reviewing a product that promises to achieve this task. It’s called Listly. You’ve probably never heard…

A step-by-step guide that goes through data exploration, feature engineering, model training, and fine-tuning — You’ve probably heard about the Titanic challenge on Kaggle. No? Don’t worry: it’s a competition where the goal is to predict a given passenger’s survival or death based on a set of variables (i.e. features). These variables include information such as age, gender, or class on the boat.

An overview of the latest release of Tweepy — Accessing external data from social networks like Twitter is valuable in many situations. Whether you’re a big company or an individual, this helps conduct and enrich analyses on particular topics, monitor the competition, evaluate the social reputation of a given product, analyze customer reviews, follow reactions to recent events, etc. …

Towards building, shipping, and deploying your apps with a peace of mind — Docker is a great tool to easily develop your applications, ship them into containers and deploy them everywhere. In my job as a data scientist, I frequently use Docker whenever I need to package an application that has multiple services underneath (such as a back-end, a database, a front-end, etc)…