Skill Path: Data Cleaning with Python
In this path, we cover the essential Python programming skills required to get started with data cleaning in Python. From there we'll use pandas and NumPy, the two most popular open-source Python libraries for data manipulation and analysis. We'll also learn the fundamentals of data visualization, a useful tool for identifying quality issues with data. From there we use fundamental tools for cleaning data in Python. Topics include manipulating data, combining data, reshaping data, and handling missing values.
Learn Data Cleaning with Python
Here's what you'll learn to do.
Dataquest Skill Paths teach you job-ready skills that can be immediately applied to your current or future data roles and projects.
- The fundamentals of programming in Python
- How to clean and visualize data
- How to combine, transform, and reshape data
- Strategies for working with missing data
Data Cleaning with Python
Python for Data Science: Fundamentals
Learn the basics of Python programming and data science.
Python for Data Science: Intermediate
Learn important tools for your Python data science toolbox.
Pandas and NumPy Fundamentals
Learn how to analyze data using the pandas and NumPy libraries.
Data Visualization Fundamentals
Learn the fundamentals of data visualization in Python by striking a good balance between graph interpretation (statistics) and tooling (Matplotlib and Seaborn).
Data Cleaning and Analysis
Learn data cleaning and analysis with pandas, how to combine data sets, and how to clean string and handle missing data.
Learn how to use regular expressions to clean and manipulate text data, how to use lambda functions and lists comprehension with pandas, and strategies for working with missing data.