🌷🌼🌸🌻🌿🌺🌱🌞🌈🦋🐝🍃🌞🌷🌼🌸🌻🌿🌺🌱🌞🌈🦋🐝🍃🌞🌷🌼🌸🌻 🌱🐍 Exciting News! PyData Cluj-Napoca 18th Spring Edition 🐍🌱 🌷🌼🌸🌻🌿🌺🌱🌞🌈🦋🐝🍃🌞🌷🌼🌸🌻🌿🌺🌱🌞🌈🦋🐝🍃🌞🌷🌼🌸🌻

We're thrilled to announce that the PyData Cluj-Napoca meetup is back with its 18th edition! After a brief hiatus, we're reviving our tech sessions, just like old times. As always, we have two engaging presentations lined up for you.

Join us for a social and informative evening – it's fantastic to be back!

---------------------------------------------------------------------------------------------------- "Improving the SQL code quality with SQLFLUFF rules" by Cristina Bocan

Nowadays the quantity of data that is processed daily has increased significantly compared to 15 years ago. The data engineers and data scientists, as well as the machine learning engineers, need to write complex SQL scripts to transform the data in the way it is required by the business. In this process of writing SQL scripts an important amount of time is taken by code review. Here the SQLFluff comes in place as a helpful tool for reducing the time spent on code review! SQLFluff is a code analyzer that checks for programmatic errors, stylistic errors or any kind of errors in a SQL script and has the ability to fix certain error types allowing the developers to focus on SQL developing part. It is implemented in Python and contains multiple sets of rules that are verified against SQL scripts. Apart of the already implemented rules it gives to the developer the possibility of implementing custom rules required by the business context. In this presentation I am going to present a particular business context in which creating a new rule was necessary and how I implemented this custom rule. Apart of that I will present how SQLFluff interacts with the SQL code and fixes it.

---------------------------------------------------------------------------------------------------- "Interactive data science for biotech: a case study on Alzheimer's research with R Shiny" by Oana Florean

Through this presentation I want to highlight the role of interactive data science in biotech research. Accessible data, real-time analysis, dynamic visualizations and collaborative work is what biotech researchers need. Recently, we had the opportunity to contribute to Alzheimer's research by developing a platform for biomarkers data exploration and analysis. As R is widely used in biotech, we embraced its versatility and developed in Shiny, a R library which makes possible the development of data science apps without much web development knowledge. We made accessible sophisticated statistical analysis to researchers, regardless of their coding proficiency. Join me to know how nicely an interactive dashboard can be created using Shiny straight from R and hear about its Python equivalent.

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