Self-driving car built for the Renault DIY Robocar Grand Prix
Hello ! I'm Subramanian Veerappan (did you skip my name ? It's okay, you can call me Subra). I'm a full stack data professional with experience in data collection, processing, storage, querying, visualization, prediction and deployment.
Uff, that sounds like a lot right, but yeah, when you work in a startup, it's important to step up to the needs of the organisation and that's how you learn new things. Thanks to my Software Engineering background, which helps me pick up technologies faster.
While I have worked with Machine learing, Data Analytics, Data engineering, & MLOps, my forte lies with Data Science & Analytics
Data + Python + Algorithm = Love ❤️.
Want to export data from Snowflake to G-Sheet ? There are some existing tools like Coefficient, but they are pricy and has some limitation when it comes to efficiency & fetching data from multiple tables. Hence, I developed SnowSheet. SnowSheet helps democratise data by making it available in G-Sheets.
With SnowSheet, you can write a SQL query as usual in the Snowflake UI, combining multiple tables, conditions, etc. All you need to do is add the link of the worksheet to SnowSheet, along with the target G-Sheet. Based on the frequency specified, SnowSheet excutes the query and writes the resultant data to the target G-Sheet.
Magicpot, as the name suggests, it's a collection of disconnected modules, that share the common objective of easing the life of data professionals.
Have you tried accessing G-Sheets with Python ? It requires tons of libraries to be imported everytime before we can access the G-Sheet. As a data professional, you might have to write the output data from a Jupyter Notebook to a G-Sheet often and you don't want to waste time importing the same libraries over and over again.
Instead you can use Magicpot, a python package that enables you to read and write data into G-Sheets with just 2 lines of code.
Another module inside Magicpot is xBridge, as way to share data between different notebooks in Deepnote project
I have successfully implemented ML projects ranging from churn prediction to recommendation systems, with a track record of delivering impactful solutions to complex data-driven challenges.
With a strong background in statistics from my engineering background and analytical experience, I've honed my ability to distill complex data into actionable insights, balancing technical expertise with practical application.
I have over 9 years of experience working with Python, leveraging it for diverse applications in data analysis, machine learning, and general software development.
I possess advanced data modeling skills using dbt and Snowflake, ensuring efficient transformation of raw data into structured, analytics-ready datasets for actionable insights.
I specialize in data visualization using Tableau, transforming complex datasets into intuitive and impactful visual narratives for informed decision-making.
My experience in Software engineering comes handy in architecting MLOps solutions, ensuring streamlined and scalable model deployment workflows