Python for Data Science, AI & Development

In this module, you will explore essential Python data structures including lists, tuples, dictionaries, and sets. Starting with lists and tuples, you will learn how to store and manipulate collections of data using indexing, slicing, and sorting techniques. Through hands-on labs, you’ll practice key operations such as cloning lists and performing tuple manipulations. The module then introduces dictionaries, where data is stored in key-value pairs, and you will gain practical experience in creating and working with them. Finally, you will examine sets, an unordered collection that contains only unique elements, and learn how to perform set operations and logic-based tasks. By the end of this module, you’ll have a strong foundational understanding of these core Python data structures.

In this module, you will build a strong foundation in core Python programming concepts essential for applied data science. The module begins with conditions and branching, where you’ll learn to use comparison and logical operators to control the flow of your program. You will then move on to loops, including for and while loops, to iterate over sequences and perform repetitive tasks efficiently. Next, the module covers functions, teaching you how to use built-in Python functions and define your own function to structure and reuse code effectively. You’ll also explore exception handling, a critical concept that enables your program to handle errors gracefully and maintain robustness. Finally, you’ll be introduced to objects and classes, the foundation of object-oriented programming in Python. You’ll learn how to define your own classes and create objects, understand attributes and methods, and see how real-world problems can be modeled using OOP principles.

In this module, you’ll begin by understanding the fundamentals of working with data in Python, focusing on how to read and write data to files in various formats such as text, CSV, and JSON. You’ll learn how to open, read, write, and manipulate files efficiently, which is essential for handling real-world data. As you progress, you’ll explore essential Python libraries for data manipulation and mathematical operations. Key libraries such as Pandas help you work with structured data in tabular formats, whereas NumPy supports numerical operations on arrays and matrices. By the end of the module, you’ll be equipped with the skills to efficiently handle, manipulate, and perform mathematical operations on data using Python, setting a strong foundation for more advanced data analysis techniques.

This module explores various techniques for collecting data, focusing on the use of APIs, web scraping, and working with different file formats. By the end of this module, you will be equipped with the necessary tools and knowledge to collect data from different sources, both structured and unstructured. The module includes hands-on labs, optional content for further exploration, and a final comprehensive exam to test your overall understanding of the course.