An introduction to Data Science using Snowflake

Presentation: Introduction to Data Science with Snowflake

Workshop description

In this session, you will learn how Snowflake can enable and accelerate end-to-end Data Science workflows. We will train and deploy a model using the Snowpark library for Python and build a Streamlit app that allows business users to consume the results of our model predictions in a simple web app - all using pure Python, without any front-end experience required.

Requirements

• Git
• Python 3.8
  • Note that we will be creating a Python environment with 3.8
• Miniconda
• A Snowflake account with Anaconda Packages enabled by ORGADMIN. If you do not have a Snowflake account, you can register for a free trial account. Choose the Enterprise edition.

Usage

• Clone the repository and navigate to the workshop folder
• Install the required libraries with conda: conda env create -f jupyter_env.yml
• Activate new environment: conda activate snowpark_pyladies
• Update connection.json with your Snowflake credentials
• Install the Snowflake extension for VS code
• Run the scripts and the notebooks
• After deploying the model, open a terminal window and run streamlit run 3-Snowpark_Streamlit_Revenue_Prediction.py
• Congratulations! You have just created a cool app to showcase your predictive powers 🚀

Quick Recap ⛄ : You've successfully performed data engineering tasks and trained a Linear Regression model to predict future ROI (Return On Investment) of variable advertising spend budgets across multiple channels using Snowpark for Python and scikit-learn. And then you created a Streamlit application that uses that model to generate predictions on new budget allocations based on user input.

Video record

Re-watch this YouTube stream

Keep-up the learning

For an up-to-date versions, please refer to the QuickStart Guide.

Credits

This workshop was set up by @pyladiesams and @avecaile and @iamontheinet