Description

In this tutorial, you’ll focus on one of the most crucial steps in any data project — data cleaning. Raw data is often messy, incomplete, inconsistent, or incorrectly formatted. Before any meaningful analysis or modeling can happen, this data must be cleaned and prepared.

You’ll begin by learning how to detect and handle missing values — whether through removal, imputation, or replacement with default values. You’ll explore different strategies depending on the type of data (numeric, categorical, or text) and the percentage of missing data in a column.

Next, you’ll work with duplicate data by identifying and removing repeated rows, which often arise from data merging or collection errors. You’ll also learn how to spot inconsistent formatting, such as mixed letter cases, unexpected whitespace, or improperly formatted strings and numbers.

A key part of this tutorial will be data type correction. You’ll practice converting columns to appropriate types (e.g., strings to dates, floats to integers) and understand how incorrect types can cause problems during analysis. You’ll also learn to identify and correct outliers and anomalies using simple statistical methods or visualizations like box plots.

The tutorial will introduce techniques for standardizing and normalizing values — such as unifying different representations of the same category (e.g., “USA” vs “United States”) or scaling numerical features when needed for machine learning.

In the hands-on section, you’ll work with a dirty dataset and go through the full cleaning process: checking for nulls, removing duplicates, fixing data types, cleaning text fields, and correcting inconsistent entries. You’ll use pandas and some basic tools from scikit-learn for preprocessing.

By the end of the tutorial, you’ll understand how to systematically approach data cleaning, make your datasets analysis-ready, and avoid common pitfalls that lead to misleading results or broken models.

You’ll also receive a Jupyter Notebook with step-by-step cleaning examples, a PDF checklist for common cleaning tasks, and practice exercises with solutions to reinforce your skills.