Getting Started with Polars for Finance

Analyze CrowdStrike Stock Data Using Polars in Python

What you'll learn
Downloading stock data for a specific publicly traded company.
Ingesting data into a dataframe.
Cleaning and analyzing stock data.
Creating visualizations to help with making financial decisions.
Performing financial calculations on stock data.

Requirements
Python basics.

Description
Learn how to analyze financial data with Polars, one of the fastest-growing Python libraries for data processing and analysis. Tailored specifically for professionals in finance, this course equips you with the skills to automate and enhance your workflows, whether you're analyzing stock market trends or investigating financial instruments.Led by expert instructor Joram Mutenge, the course introduces you to Polars’ advanced capabilities through clear, concise, and actionable lessons. Each section is carefully designed to facilitate quick learning, ensuring you grasp the material efficiently and effectively.What You'll Learn:Data Preparation and Ingestion: Seamlessly import, clean, and preprocess financial data.Technical Analysis Signals: Code and interpret indicators to identify market trends.Visualizing Financial Insights: Create compelling interactive charts for informed decision-making.Optimized Calculations: Use Polars' cutting-edge features to perform advanced calculations such as On-balance volume and exponential moving averages with speed and precision.Best Practices: Implement efficient workflows to handle large datasets effortlessly.By the end of this course, you'll have the confidence and expertise to leverage Polars in your financial analysis, transforming raw data into actionable insights. If you’re already a finance professional, this course will empower you to be more efficient at your work and stay ahead in a data-driven world. If you’re just beginning your journey in finance, this course will help you think about how to automate your work rather than relying on manual Excel work.

Who this course is for
People working in finance who are interested in automating their stock analysis workflows.
Data analysts interested in learning how to process and analyze timeseries data.