Data processing is a critical skill in today's data-driven world, and NumPy and Pandas are two of the most widely used Python libraries for data manipulation, analysis, and visualization. This course will provide an in-depth introduction to the NumPy and Pandas libraries and their use in data processing.

In this course, you will learn how to use NumPy and Pandas to read, clean, manipulate, and analyze data from a variety of sources. You will learn the fundamental data structures of NumPy and Pandas and how to use them to perform operations like filtering, sorting, merging, grouping, and aggregating data.

You will also learn how to handle missing data and perform basic statistical analysis.

Throughout the course, you will work on real-world datasets and practical examples to gain hands-on experience with the NumPy and Pandas libraries. By the end of the course, you will have a solid understanding of how to use NumPy and Pandas to process and analyze data effectively. In addition, you will complete three final projects to apply the concepts you've learned and showcase your skills.

Course Objectives:

• Understand the fundamental data structures of NumPy and Pandas
• Learn how to read, clean, and manipulate data using NumPy and Pandas
• Use NumPy and Pandas to perform filtering, sorting, merging, grouping, and aggregating data
• Handle missing data and perform basic statistical analysis
• Apply NumPy and Pandas to real-world datasets and practical examples
• Complete several final projects to apply the concepts learned in the course

Prerequisites:

• Familiarity with Python programming language
• Basic knowledge of statistics and probability

This course includes a crash course for Python and statistics to help you get up to speed quickly.

Target Audience:

• Data analysts
• Data scientists
• Data engineers
• Business analysts
• Anyone interested in data processing and analysis with Python.