This specialization teaches learners how to efficiently manipulate, transform, and analyze data using Python and Polars. Starting with core DataFrame operations, learners work with row and column selection, slicing, type casting, missing data handling, sorting, filtering, and data cleaning techniques to prepare structured datasets for analysis.
The program then advances to more powerful Polars features, including joins, concatenations, reshaping, aggregations, group-by operations, selectors, and lazy evaluation for improved performance. Learners also gain experience working with arrays, lists, structs, text data, categorical variables, and datetime fields to handle complex real-world datasets.
Through hands-on examples and interactive support from Coursera Coach, learners build practical skills for creating efficient and scalable data workflows. Designed for beginners to intermediate Python users, the specialization requires only basic Python knowledge and no prior Polars experience.
By the end, learners will be able to extract, transform, and analyze data effectively while leveraging Polars to solve real-world data challenges.
Applied Learning Project
Learners will complete hands-on projects that include filtering and transforming datasets, performing joins and aggregations, reshaping data, and implementing lazy pipelines. These projects simulate real-world data analysis tasks, enabling learners to apply Polars and Python skills to optimize workflows and generate actionable insights.
