Data analysts and data scientists alike report that while there are definitely “sexier” parts of the job, most of their time is spent on data preparation and cleaning.

In our advanced data cleaning course, you’ll learn how to supercharge your workflow with some advanced data cleaning techniques that will make you a data analysis superstar.

You'll learn about regular expressions (regex), a powerful tool that allows you to match and manipulate text data with a lot of precision. You'll also learn to work with JSON data, a common format you'll encounter when pulling data from web APIs.

Then you’ll dive into list comprehensions and lambda functions, two intermediate-to-advanced Python concepts that are extremely useful for working with data and that can speed up your data cleaning work.

You’ll also learn how to handle missing values in your data, a critical part of almost every data analysis project. Rather than dropping rows or columns, which reduces the amount of data you have to work with, you'll learn statistical techniques to impute missing data, and you'll also learn how to insert data from outside sources.

By the end of this course, you'll be able to:

  • Use regular expressions to clean and manipulate text data.
  • Use lambda functions and list comprehension with pandas.
  • Work with missing data.

Learn Advanced Data Cleaning In Python

Regular Expression Basics

Learn to perform data cleaning with regular expressions

Advanced Regular Expressions

Describe complex patterns in text data for cleaning and analysis

List Comprehensions and Lambda Functions

Learn techniques to turbo-charge working with data in Python

Working with Missing Data

Identify and deal with missing and incorrect data