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⇱ Data Science with Python | Coursera

Data Science with Python

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

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Master data manipulation with NumPy and Pandas to process large datasets.

  • Create powerful visualizations using Matplotlib and Plotly to explore data trends.

  • Understand image processing techniques for manipulating and analyzing images in Python.

  • Gain hands-on experience with PyTorch to work with tensors and machine learning models.

Details to know

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Recently updated!

April 2026

Assessments

8 assignments

Taught in English

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This course is part of the The Complete Python and Data Science Bootcamp 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 6 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 Data Science with Python course, you will master essential libraries such as NumPy, Pandas, Matplotlib, and PyTorch to solve real-world data science challenges. Starting with NumPy, you’ll learn how to work with arrays, perform linear algebra, and manipulate large datasets. You’ll then explore Pandas to filter, analyze, and visualize data efficiently, followed by Matplotlib for creating informative plots and visualizations that uncover patterns in data. As you progress, you will dive into advanced image processing techniques with Matplotlib, build interactive plots using Plotly, and gain hands-on experience with PyTorch fundamentals. The course will guide you through essential concepts like tensors, GPU acceleration, broadcasting, and model training, offering a solid foundation for machine learning and deep learning tasks. Designed for individuals eager to advance their data science skills, this course is ideal for beginners and intermediate learners. With practical exercises, real-world applications, and interactive lessons, you'll be prepared to tackle any data science project. Upon completion, you'll be ready to take your skills further in the field of machine learning and artificial intelligence. By the end of the course, you will be able to manipulate data with NumPy and Pandas, visualize data using Matplotlib and Plotly, process images, and implement machine learning models using PyTorch.

In this module, we will explore NumPy, a powerful library for numerical computing in Python. You'll learn how to create and reshape arrays, perform element-wise operations, and dive into linear algebra applications. Advanced topics such as solving linear systems and logical filtering will also be covered.

What's included

11 videos2 readings1 assignment

11 videos•Total 83 minutes
  • NumPy Arrays, Shape, and Reshape•11 minutes
  • NumPy Arrays of Zeros, Ones, and the Identity Matrix•6 minutes
  • Empty and Random•4 minutes
  • Indexing and Slicing in NumPy•11 minutes
  • Arithmetic and NumPy•10 minutes
  • Rough Idea of Linear Algebra and Its Applications•6 minutes
  • (Advanced) Concepts from Linear Algebra in NumPy•16 minutes
  • Solving Linear Systems•6 minutes
  • Logic: Element-Wise Comparison•3 minutes
  • Logic: Comparison with Scalars•2 minutes
  • Logic: Filtering and Where•9 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Data Science With Python'•10 minutes
  • Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
  • NumPy - Assessment•15 minutes

In this module, we will work with Pandas to process and analyze real-world datasets. You’ll learn how to filter data, manage missing values, and manipulate columns. The Titanic dataset will serve as an example to practice essential Pandas operations and data wrangling techniques.

What's included

8 videos1 assignment

8 videos•Total 49 minutes
  • Getting Started with Pandas: Titanic Dataset Analysis•8 minutes
  • Filtering•4 minutes
  • Filtering and the isin Operator•5 minutes
  • Filter Rows Using notna•2 minutes
  • Examples of Filters and Logic•16 minutes
  • Solutions to the Filtering Exercises from the Previous Lecture•6 minutes
  • Filtering Columns•1 minute
  • Applying concat to Two Series•7 minutes
1 assignment•Total 15 minutes
  • Pandas - Assessment•15 minutes

In this module, we will cover data visualization using Matplotlib. You will create a wide range of plots, from simple bar charts to complex 3D surface plots, and learn how to enhance them with annotations, colors, and advanced graphing techniques for statistical analysis.

What's included

20 videos1 assignment

20 videos•Total 106 minutes
  • Simple Bar Plot•3 minutes
  • Bar Plot—Calories per Day•3 minutes
  • Box Plot•7 minutes
  • Real-World Scenario: Customer Satisfaction Analysis—Box Plot•3 minutes
  • A Simple Scatter Plot•3 minutes
  • Scatter Plot Example—Average Daily Temperatures and Ice Cream Sales•2 minutes
  • Comparing Groups with Scatter Plots•6 minutes
  • Graphing a Function with Scatter Plot•4 minutes
  • Graphing Lines•2 minutes
  • Text Annotations•6 minutes
  • Linear Regression•13 minutes
  • Histograms•4 minutes
  • Subplots•2 minutes
  • Multiple Subplots with Different Colors and Titles•8 minutes
  • Enhancing Titles Using LaTeX•4 minutes
  • Image Subplots•5 minutes
  • Pie Chart•8 minutes
  • Stack Plot•7 minutes
  • Bar Chart•8 minutes
  • 3D Plot Using a Mesh Grid•6 minutes
1 assignment•Total 15 minutes
  • Matplotlib, Graphing, and Statistics - Assessment•15 minutes

In this module, we will combine Matplotlib and NumPy to process images. You’ll learn how to manipulate image channels, apply basic transformations like grayscale conversion and thresholding, and experiment with advanced techniques like image compression and tiling for creative results.

What's included

10 videos1 assignment

10 videos•Total 71 minutes
  • Loading an RGB Image•5 minutes
  • Extracting RGB Channels•7 minutes
  • Converting an RGB Image to Grayscale•8 minutes
  • Exploring Color Maps•3 minutes
  • Creating n by n RGB Images•10 minutes
  • Image Manipulation—Thresholding•6 minutes
  • Image Manipulation—Compression•12 minutes
  • Image Manipulation—Squeeze Image•12 minutes
  • Image Manipulation—Inverting Images•6 minutes
  • Image Manipulation—Image Tiling•2 minutes
1 assignment•Total 15 minutes
  • Matplotlib and Image Processing - Assessment•15 minutes

In this module, we will explore Plotly to create interactive and visually engaging plots. You’ll build line and scatter plots with tooltips, experiment with 3D visualizations, and learn how to customize plots using advanced techniques like graph objects and dictionary-based figure construction.

What's included

6 videos1 assignment

6 videos•Total 36 minutes
  • Interactive Line Plot•6 minutes
  • Line Plot Modes•5 minutes
  • Interactive Scatter Plot with Tooltips•7 minutes
  • Interactive 3D Surface Plot•6 minutes
  • Figures as Dictionaries•7 minutes
  • Figures as Graph Objects•5 minutes
1 assignment•Total 15 minutes
  • Plotly and Interactive Plots - Assessment•15 minutes

In this module, we will introduce you to PyTorch, a powerful deep learning framework. You will learn how to work with tensors, perform various tensor operations, and explore the concepts of broadcasting. Additionally, you will discover how to use GPU acceleration to optimize performance for large-scale computations.

What's included

13 videos1 reading3 assignments

13 videos•Total 97 minutes
  • Google Colab and tqdm•4 minutes
  • Getting Help•5 minutes
  • Getting More Help•4 minutes
  • Introducing PyTorch and Tensors 1•10 minutes
  • Introducing PyTorch and Tensors 2•9 minutes
  • Using the GPU•2 minutes
  • Operators and More Operations•13 minutes
  • Indexing and Masking•14 minutes
  • Masking Continued•14 minutes
  • Cloning Tensors•4 minutes
  • Broadcasting—First Steps•5 minutes
  • Broadcasting Continued•8 minutes
  • More Broadcasting Examples•4 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Data Science With Python'•10 minutes
3 assignments•Total 90 minutes
  • PyTorch Fundamentals - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

Data Science with Python is a course designed to teach how Python can be used for data analysis, manipulation, and visualization. Python is one of the most widely used programming languages in the field of data science, thanks to its rich ecosystem of libraries such as NumPy, Pandas, Matplotlib, and PyTorch. Learning how to use Python for data science is relevant because it allows individuals to analyze large datasets, create models, visualize trends, and make data-driven decisions. Python’s popularity in data science is driven by its simplicity, flexibility, and powerful libraries that make data processing and analysis easier.

This course covers essential data science techniques using Python. You will start with the basics of NumPy for numerical computing, followed by using Pandas for data manipulation and cleaning. The course also introduces data visualization using Matplotlib and interactive plotting with Plotly. You'll explore image processing and PyTorch fundamentals for machine learning. By the end of the course, you will have the skills to analyze data, create meaningful visualizations, and understand the basics of deep learning using PyTorch.

After completing this course, you will be proficient in using Python for data science tasks such as data cleaning, manipulation, and analysis. You will be able to create visualizations to interpret and present data effectively, use machine learning tools with PyTorch, and handle large datasets with libraries like NumPy and Pandas. You will also have hands-on experience working with interactive plots and performing basic image processing, giving you a comprehensive skill set to work on data science projects.

This course is designed for individuals with a basic understanding of Python programming. If you are comfortable with basic Python concepts such as variables, loops, and functions, you will be ready to dive into the data science topics covered in this course. No prior knowledge of data science or machine learning is required, but familiarity with Python syntax will help you grasp the course content faster.

This course is ideal for anyone interested in pursuing a career or a hobby in data science. It is specifically suited for those who have some experience with Python programming and want to apply it to analyze and visualize data. If you want to explore data science tools and techniques, or if you're aiming to transition into data science or machine learning, this course will provide the necessary foundation to get started.

The course is designed to take approximately 10 hours to complete. This includes video lectures, coding exercises, and hands-on projects. The course is structured so that you can learn at your own pace, and most learners will be able to finish the material in about a week if they dedicate a few hours each day to the content.

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,