Data Wrangling with Python, NumPy, and pandas

Raw data is rarely ready for analysis, often containing missing values, inconsistent formatting, and structural issues. Learning how to clean and prepare this data is the most critical step in any data professional's workflow. This text-based course guides you from raw, chaotic datasets to clean, structured data ready for modeling. You will build a solid foundation in data manipulation using industry-standard Python libraries. What you'll learn: Understand foundational data structures and how data is represented in Python; Clean messy datasets by handling missing values, duplicates, and incorrect data types; Manipulate multi-dimensional arrays efficiently using NumPy; Filter, group, and aggregate complex datasets using pandas; Apply modern data wrangling practices, including basic type hinting and memory-efficient data loading; Reshape and merge multiple data sources into unified formats. You will start with core concepts of data structures before moving into hands-on manipulation with NumPy and pandas, learning through clear written explanations and practical code examples. Designed for absolute beginners to data analysis, with no prior data wrangling experience required. Start reading today to build the essential data preparation skills that every data professional needs.