Pandas and NumPy Fundamentals
In our Pandas and NumPy fundamentals course, you will learn how to work with Pandas and NumPy, the two most popular Python open-source libraries for data analysis.
To start off this course, you’ll learn about NumPy and how to work with data using the library. That means digging into concepts such as vectorized operations and going hands-on with selecting data from NumPy ndarrays. To enhance your knowledge of NumPy, you'll then learn how to select data based on their values using a technique called boolean indexing.
After you learn about NumPy, we’ll jump into pandas, which is the most popular open source library for data science. Along with learning about the pandas library, you'll learn how it can work with NumPy to supercharge your data analysis workflow.
Once you’ve mastered the basics of NumPy and pandas, you will get some practice exploring data using the pandas library and learning how to select data in a pandas dataframe, how to filter your data, and more pandas analytical techniques. You’ll also learn more data cleaning techniques, using pandas to make your data easier to work with.
Finally, you will apply all the skills you learned in a guided project exploring real eBay Car Sales data to that makes an excellent test of your new skills, and you can put the final results into your portfolio to impress future employers!
By the end of this course, you'll be able to:
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Learn to use the Pandas and NumPy Libraries for Data Analysis
Introduction to NumPy
Learn how to work with data using the NumPy library.
Boolean Indexing with NumPy
Learn how to select data based on their values.
Introduction to Pandas
Learn how to use the pandas library to work with data.
Exploring Data with Pandas: Fundamentals
Learn how to use the pandas library to work with data.
Exploring Data with Pandas: Intermediate
Learn more techniques for selecting and analyzing data in pandas.
Data Cleaning Basics
Learn how to clean messy data using Pandas.
Exploring Ebay Car Sales Data
Practice data cleaning and data exploration using Pandas.