Build the data preparation skills you need to turn raw, messy data into clean, model-ready datasets. In this course, you’ll develop practical experience used in roles such as data analyst, junior data scientist, machine learning analyst, business analyst, and analytics engineer. You’ll work through the process of ingesting data from files, databases, and APIs, auditing data quality, performing exploratory data analysis, and creating visualizations that help you understand what the data needs before modeling begins.

This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from combining data from multiple sources and diagnosing data quality issues to preparing training, validation, and test datasets for machine learning workflows. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data preparation experience. By the end, you’ll be able to ingest and assess raw data, clean missing and inconsistent values, detect and treat outliers, engineer meaningful features, and prepare properly split, scaled, normalized, and encoded datasets for analysis and modeling. This course is a strong fit if you already have basic experience with data analysis, spreadsheets, SQL, Python, or introductory machine learning concepts.