The 8 best Data Cleaning & Wrangling courses in 2026

Expand your understanding of H2O's GenAI platform with the Ecosystem Overview - Level 2 course, presented by H2O's very own Sanyam Bhutani! This course provides an in-depth comprehensive exploration of the tools, applications, and methodologies tailored for artificial intelligence (AI) and machine learning (ML) within the H2O ecosystem. You will delve deeper into efficient data preparation techniques, advanced model training, deployment strategies, and real-time monitoring solutions, covering the full spectrum of all the capabilities available. Through engaging modules, you will soon master t

This course guides you through the process of transforming raw financial data into a clean, trustworthy dataset using Python and pandas. You’ll begin by exploring how to load data into a notebook environment and conduct quick inspections to identify structural issues, formatting inconsistencies, unusual numeric patterns, and missing values. Building on these observations, you’ll apply essential cleaning techniques used by analysts every day—fixing data types, standardizing text categories, resolving or documenting missingness, and removing duplicates. Through guided walkthroughs, hands-on prac

Cloud Dataprep is Google's self-service data preparation tool. In this lab, you will learn how to use Cloud Dataprep to clean and enrich multiple datasets using a mock use case scenario of customer info and purchase history.

In Data Cleaning, Transformation, and Manipulation, you’ll learn to turn messy data into analysis- and modeling-ready datasets using Python (pandas) and SQL. This is a skill-based path organized around real workplace tasks. Each module mirrors responsibilities you see in job descriptions and focuses on the exact steps you’ll perform on the job. You’ll begin with a quick skills check, then personalize your journey: double down on new topics, or skip what you already know. For each skill, you’ll review concise lessons curated from expert instructors with explanations and demos for filtering and

University of Michigan's beginner-friendly path covering Python programming, data structures, web data, and databases.

Google's job-ready data analytics path: spreadsheets, SQL, Tableau, R, and data storytelling.

Interactive track covering pandas, data cleaning, visualization, and exploratory analysis.

Plan, capture, and manage high-quality research data — electronic data capture, data strategy, and study workflows.