Polars for Data Science: Tackling Real-World Data Challenges

Unlock the full potential of your data workflow with “Polars for Data Science”! In today's rapidly evolving data-landscape, efficiency and flexibility are paramount; take this course to find out exactly why Polars is essential for the modern data practitioner.

Through hands-on exercises, you'll learn to seamlessly replace Pandas with Polars, harnessing its speed and versatility to clean and manipulate data of all types. From basic operations to advanced techniques, you'll master everything from filtering and sorting, to aggregation and joining across various data types, including numeric, string, array, and datetime data. Delve into Polars's innovative expression API and explore its streaming and in-memory capabilities.

Moreover, our course doesn't just stop at data manipulation—we guide you through a practical data science use-case, integrating Polars with your favorite visualization tools and with training machine learning models.

By the end, you'll be equipped to tackle real-world data challenges with ease. Join us and elevate your data skills to the next level!

What you’ll learn and how to apply it

After each of the course modules, students will…

• Understand what makes Polars a groundbreaking technology for data science, where it sits in the landscape of data science tooling, and have an intuition of the syntax differences between Polars, Pandas, and SQL.
• Be familiar with Polars's two modes, lazy-mode versus in-memory mode, and understand how to leverage Polars's query optimization.
• Be familiar with the Polars API, and be able to perform basic selecting and filtering queries.
• Be able to perform Polars queries that involve aggregations, such as group-by's and window functions.
• Be able to perform Polars queries that involve combining data, such as joins and concatenations.
• Understand all the different data types that exist in Polars, and how to work with them.
• Be able to switch their data seamlessly between Pandas, Numpy, and Polars, and be able to read and write data both locally and to the cloud.
• Be comfortable using Polars in a typical everyday data science workflow, integrating it with Matplotlib, Plotly, and Scikit Learn.
• Have their understanding of the different facets of Polars unified into one clear picture, and be prepared with all the knowledge and reference resources they need to continue using Polars on an everyday basis while growing their knowledge of the tool.

This course is for you because

• You are a Data Scientist, Data Analyst, Data/ML Engineer, or Python Developer.

Prerequisites

This course assumes familiarity with Python. It also assumes that the audience members have a basic understanding and familiarity with data manipulation and/or machine learning, regardless of the tool stack (e.g. Python Pandas, R, SQL).

Course Materials