Data Science

Leveraging data to help users discovering new content — Recommender Systems: Why And How? Recommender systems are algorithms providing personalized suggestions for items that are most relevant to each user. With the massive growth of available online contents, users have been inundated with choices. …

It’s never a bad idea to reevaluate the way we approach our work. This is true whether you’re a student working on a portfolio project, a seasoned ML engineer managing an end-to-end pipeline, or an executive responsible for the success of an entire data team. It could be a superfluous…

The Data Engineering field is popular nowadays and many tools are available in the open source world. So first let’s see the key processes of the data engineering pipeline and then tools process wise. Ingestion → Storage → Transformation → Explore and analyze Workflow management Tools: Apache Airflow

A data quality use case with advanced EDA — Visualization is the cornerstone of EDA. When facing a new, unknown dataset, visual inspection allows us to get a feel of the available information, draw some patterns regarding the data, and diagnose several issues that we might need to address. In that regard, Pandas Profiling has been the indispensable swiss-knife…

tl;dr: In this post, we compare Pandas vs. SQL on the third of three axes: convenience. We describe six ways in which the Pandas dataframe data model is more convenient for data science and machine learning use cases. In this fourth offering of our epic battle between Pandas vs. SQL…