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Python For Data Analytics
Course Introduction:
Welcome to Python for Data Analytics!
A practical course designed to introduce learners to the powerful capabilities of Python in handling and analyzing data.
This course focuses on core data analytics techniques such as data cleaning, transformation, and statistical analysis using widely adopted Python libraries like NumPy and Pandas. Through hands-on exercises and real-world datasets, you will develop the skills to extract meaningful insights and build a strong foundation for data-driven decision-making.
To reinforce learning, two real-world data projects are included at the end that allow you to apply your skills to solve practical analytical problems.
Click here to access all the datasets used in the exercises throughout the course.
NumPy Essentials
NumPy (Numerical Python) is a foundational library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of high-performance mathematical functions to operate on these arrays.
In this course, you will learn how to use NumPy for efficient data storage, manipulation, and computation — skills that are essential for any data analytics workflow.
Pandas Essentials
Pandas is a powerful library for data manipulation and analysis. It introduces two primary data structures, Series and DataFrame, that make it easy to clean, transform, and explore structured data.
In this course, you will use Pandas to handle missing data, filter and group datasets, merge multiple data sources, and perform descriptive statistics, all of which are crucial for preparing data for analysis.