Cleaning Data in Python

Discover How to Clean Data in Python It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it. Data cleaning is an essential step for every data scientist, as analyzing dirty data can lead to inaccurate conclusions. In this course, you will learn how to identify, diagnose, and treat various data cleaning problems in Python, ranging from simple to advanced. You will deal with improper data types, check that your data is in the correct range, handle missing data, perform record linkage, and more! Learn How to Clean Different Data Types The first chapter of the course explores common data problems and how you can fix them. You will first understand basic data types and how to deal with them individually. After, you'll apply range constraints and remove duplicated data points. The last chapter explores record linkage, a powerful tool to merge multiple datasets. You'll learn how to link records by calculating the similarity between strings. Finally, you'll use your new skills to join two restaurant review datasets into one clean master dataset. Gain Confidence in Cleaning Data By the end of the course, you will gain the confidence to clean data from various types and use record linkage to merge multiple datasets. Cleaning data is an essential skill for data scientists. If you want to learn more about cleaning data in Python and its applications, check out the following tracks: Data Scientist with Python and Importing & Cleaning Data with Python.