The 8 best Pandas courses in 2026

In this course, you’ll explore data structures in Python, which are methods of storing and organizing data in a computer. You’ll focus on data structures that are among the most useful for data professionals: lists, tuples, dictionaries, sets, and arrays. You’ll also discover how to categorize data using data loading, cleaning, and binning. Lastly, you’ll learn about two of the most widely used and important Python tools for advanced data analysis: NumPy and pandas. By the end of this course, you will be able to: • Explain how to manipulate dataframes using techniques such as selecting and i

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

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

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. In this course, you'll learn how to effectively use Python for data visualization. You will start by setting up your environment and installing key libraries like Anaconda, Matplotlib, Seaborn, and Cufflinks, which are the cornerstone tools for data visualization in Python. You'll explore reading and processing data with Pandas, setting the stage for building powerful vi

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. In this hands-on course, you will learn how to use Python for data analysis through practical, step-by-step projects. You will start with Python basics, including data types, functions, and loops, and then dive into the powerful Pandas library to load, manipulate, and clean data. As you explore data, you'll master techniques like combining datasets, renaming columns, sor

Build a strong foundation in supervised machine learning by learning how to develop, evaluate, and interpret classification models using Python. In this hands-on course, you will work with the real-world Titanic dataset to explore the complete machine learning workflow, from project setup and data preparation to model evaluation and deployment readiness. You will begin by understanding the lifecycle of a supervised machine learning project, defining problem objectives, and using essential Python libraries such as NumPy and pandas. You will also explore core supervised learning algorithms, inc

Build practical numerical computing and retail data analysis skills with NumPy and Pandas. Designed for aspiring data analysts, business intelligence professionals, and Python enthusiasts, this hands-on course guides you from NumPy foundations to advanced Pandas techniques through case studies and retail datasets. You’ll learn to create and manipulate NumPy arrays using slicing, reshaping, stacking, and broadcasting; apply linear algebra operations and implement gradient descent for analytical problems. You’ll then use Pandas to import data from multiple sources, clean and transform retail da

Python, SQL, data analysis, visualization, machine learning, and a capstone project.