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⇱ Python Programming And Libraries for Data Science | Coursera

Python Programming And Libraries for Data Science

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Python Programming And Libraries for Data Science

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Master Python libraries like NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization.

  • Implement object-oriented programming principles to write clean and reusable code.

  • Gain expertise in handling different file formats and managing exceptions in Python programs.

  • Build practical applications using Python, including a simple banking system and custom exception handling.

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Recently updated!

February 2026

Assessments

5 assignments

Taught in English

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This course is part of the Google Colab for Data Science & AI using Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

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.

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.

What's included

11 videos2 readings1 assignment

11 videos•Total 112 minutes
  • Libraries in Python•5 minutes
  • Installing Libraries in Colab•8 minutes
  • Importing the Libraries•9 minutes
  • Essential Libraries: Numpy•18 minutes
  • Essential Libraries: Pandas•23 minutes
  • Essential Libraries: Matplotlib•12 minutes
  • Essential Libraries: Seaborn•9 minutes
  • Hands-on - Use NumPy to perform matrix operations•5 minutes
  • Hands-on - Load a CSV dataset into a Pandas Data Frame and perform Analysis•8 minutes
  • Hands-on - Create a line plot and a bar chart using Matplotlib to visualize data•11 minutes
  • Hands-On - Use Seaborn to create a scatter plot with a regression line•5 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Python Programming And Libraries for Data Science'•10 minutes
  • Full Specialization Resource•10 minutes
1 assignment•Total 15 minutes
  • Working with Libraries in Colab - Assessment•15 minutes

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.

What's included

16 videos1 assignment

16 videos•Total 144 minutes
  • What is OOP•5 minutes
  • Principles of OOP•5 minutes
  • Classes and Objects•9 minutes
  • Constructors•7 minutes
  • Instance Variables•8 minutes
  • Methods•12 minutes
  • Advanced OOP Concepts - Inheritance•15 minutes
  • Advanced OOP Concepts - Polymorphism•6 minutes
  • Advanced OOP Concepts - Encapsulation•11 minutes
  • Advanced OOP Concepts - Abstraction•8 minutes
  • Modules•12 minutes
  • Packages•7 minutes
  • Importing Modules & Functions•8 minutes
  • Hands On - Create a DOG class with attributes•8 minutes
  • Hands On - Create a CAT Class and demonstrate Inheritance•6 minutes
  • Hands On - Building a Simple Banking System•19 minutes
1 assignment•Total 15 minutes
  • Object-Oriented Programming (OOP) - Assessment•15 minutes

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.

What's included

13 videos1 reading3 assignments

13 videos•Total 99 minutes
  • File Handling - Opening Files•5 minutes
  • File Handling - Reading Files•9 minutes
  • File Handling - Writing Files•9 minutes
  • File Handling - Closing Files•8 minutes
  • File Handling - Working with Different file types•15 minutes
  • Errors and Exceptions•6 minutes
  • Try and Except Blocks•11 minutes
  • Raising Exceptions•6 minutes
  • Custom Exceptions•10 minutes
  • Hands On - Write a program to read a text file and count the number of words•4 minutes
  • Hands On - Write a program to read a CSV file and calculate the average of a column•4 minutes
  • Hands On - Create a program to handle potential errors•7 minutes
  • Hands On - Implement a custom exception for invalid file formats•5 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Python Programming And Libraries for Data Science'•10 minutes
3 assignments•Total 90 minutes
  • File Handling and Exception Management - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

Python Programming and Libraries for Data Science is a comprehensive course designed to teach you how to leverage Python’s capabilities for data science. It covers essential libraries such as NumPy, Pandas, Matplotlib, and Seaborn for tasks like data manipulation, analysis, and visualization. This course is relevant because Python is one of the most widely used programming languages in data science and machine learning, making it an invaluable skill for anyone interested in the field.

This course provides a deep dive into Python programming with a focus on libraries and tools that are essential for data science. You’ll learn about handling data with Pandas, performing complex mathematical computations with NumPy, and visualizing data through Matplotlib and Seaborn. You will also get hands-on experience with object-oriented programming, file handling, and exception management, all key areas for working with data in Python.

After completing this course, you will be able to write Python programs that can manipulate and analyze data efficiently. You will have the skills to use popular data science libraries, build custom data visualizations, handle files and exceptions, and apply object-oriented programming concepts. You will be equipped to work with datasets, analyze them, and visualize insights, all fundamental tasks in data science.

This course is designed for beginners, so no prior knowledge of Python or data science is required. However, a basic understanding of programming concepts or experience with any programming language could be beneficial. It is also helpful if you have an interest in data analysis or data science as a career path.

This course is ideal for beginners who are looking to get into data science or for those who wish to enhance their Python skills with data manipulation, analysis, and visualization. It’s also perfect for anyone interested in using Python for practical, hands-on data science projects.

The course consists of approximately 7 hours of video content. The actual time required will vary depending on your pace, but it can typically be completed in a week or two if you dedicate a few hours per day to learning and practice.

This course does not mention a certificate, so it may not offer one upon completion. However, you will gain valuable skills and knowledge that will aid you in your data science journey. You can use the hands-on projects and the course content to showcase your learning.

You will need to have access to a Python environment to run your code. The course specifically uses Google Colab, which is an online platform that doesn’t require installation. However, you should ensure you have access to a stable internet connection to use Colab effectively for your assignments and exercises.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,