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Introduction to Data Analysis with Python and Polars

Packt via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

7 weeks, 2 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Part of

Data Analysis with Polars and Python

Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the world of data analysis using Python and Polars, a fast and efficient library for handling structured data. This course empowers you to manipulate datasets, clean and transform data, and perform insightful analysis, equipping you with practical skills applicable in real-world data projects. You will start by setting up your environment on macOS or Windows, learning terminal basics, installing Python packages with uv, and navigating Jupyter Lab for seamless project management. Each step is designed to build your confidence and ensure a smooth workflow from the very beginning. Next, you'll explore Python fundamentals, covering variables, operators, functions, and data structures before transitioning into Polars-specific concepts. You'll learn to create Series and DataFrames, handle missing values, optimize memory, and use powerful expressions to manipulate and filter data efficiently. The course is ideal for beginners and intermediate learners interested in data science or analytics. No prior Polars experience is needed, but basic familiarity with Python will help. Anyone looking to enhance their Python-based data handling and analytical skills will find immense value here. By the end of the course, you will be able to confidently set up your data environment, perform advanced data manipulations in Polars, clean and filter datasets, join and aggregate data, and derive actionable insights from structured data using Python and Polars.

Syllabus

Introduction

In this module, we will introduce you to Polars, a high-performance data manipulation library, and set up your development environment. You will learn to navigate command-line interfaces on both macOS and Windows, install necessary Python packages, and prepare Jupyter Lab for coding. By the end of this section, you will be ready to start your data analysis journey with all tools properly configured.

Python Crash Course

In this module, we will provide a rapid, practical introduction to Python for data analysis. You will explore data types, operators, functions, and control structures while learning to manage code efficiently. By the end, you will have a solid foundation to write, run, and optimize Python code for real-world data tasks.

Series

In this module, we will dive into Polars Series, the fundamental building block for handling data in Polars. You will learn to create, inspect, and optimize Series while applying mathematical and data-cleaning techniques. By the end, you will confidently manage and analyze single-column data efficiently.

DataFrames I

In this module, we will introduce Polars DataFrames, the central structure for tabular data management. You will learn to create, import, and manipulate data while exploring expressions, selection methods, and advanced operations. By the end, you will efficiently organize, transform, and compute on complex datasets.

DataFrames II

In this module, we will focus on refining your DataFrame handling skills. You will learn to manage missing data, sort, rank, and shuffle rows while extracting meaningful insights. By the end, you will be able to clean, organize, and analyze large datasets with precision.

DataFrames III - Filtering

In this module, we will explore powerful filtering techniques in Polars DataFrames. You will learn to extract, refine, and conditionally manipulate data using logical and comparison operators. By the end, you will confidently query datasets to focus on relevant subsets for analysis.

Joins

In this module, we will teach you how to combine datasets in Polars using a variety of join operations. You will learn to perform standard and advanced joins, manage key columns, and maintain data integrity. By the end, you will be able to merge multiple datasets effectively for deeper insights.