Python Programming And Libraries for Data Science

Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you will explore Python programming with a specific focus on libraries that power Data Science. You'll gain hands-on experience with essential Python libraries like NumPy, Pandas, Matplotlib, and Seaborn, and learn how to leverage these tools in data analysis and visualization. Through engaging examples and practical exercises, you'll understand how to efficiently handle data, perform calculations, and create stunning visualizations. You'll also delve into object-oriented programming (OOP), mastering key concepts such as classes, objects, inheritance, and polymorphism. The course will guide you step-by-step through the process of writing clean, modular code while developing your problem-solving skills. Along with OOP, you'll gain valuable insights into file handling and exception management, essential for creating robust applications in Python. The course is ideal for anyone interested in Data Science, whether you're starting your programming journey or looking to enhance your skills. It is beginner-friendly, but some prior knowledge of programming concepts is helpful. The hands-on approach ensures that you can immediately apply your new skills to real-world projects and build a strong foundation in Python. By the end of the course, you will be able to use Python libraries for data manipulation and visualization, implement object-oriented principles in code, handle files and exceptions effectively, and create dynamic Python programs for real-world data analysis tasks.

Syllabus

Working with Libraries in Colab

In this module, we will explore the key Python libraries essential for data science, such as NumPy, Pandas, Matplotlib, and Seaborn. We will guide you through installing, importing, and using these libraries within Google Colab. Additionally, we will provide hands-on exercises that involve matrix operations, data analysis, and data visualization.

Object-Oriented Programming (OOP)

In this module, we will dive into Object-Oriented Programming (OOP) in Python, covering essential concepts like classes, objects, and methods. You'll learn about the four key OOP principles—encapsulation, inheritance, abstraction, and polymorphism—and how to apply them in your projects. Hands-on examples will help you master OOP through real-world applications like creating a simple banking system.

File Handling and Exception Management

In this module, we will cover the fundamentals of file handling in Python, including opening, reading, writing, and closing different types of files. You'll also learn how to manage errors and exceptions using try-except blocks, ensuring your code runs smoothly even in unexpected situations. Through hands-on examples, we will demonstrate how to handle files and create robust error management systems in your programs.