Hands-On Data Science with PyTorch & Pandas
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Hands-On Data Science with PyTorch & Pandas
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What you'll learn
Build interactive data dashboards using Shiny for Python with dynamic inputs, outputs, and visualizations for real-time data exploration.
Create CSV-based analytics apps that allow users to upload datasets, generate statistics, and visualize insights through charts and dashboards.
Apply PyTorch tensor operations including broadcasting, indexing, and GPU acceleration to support efficient machine learning workflows.
Develop an image classification app using PyTorch and TorchVision, integrating preprocessing, inference, and an interactive Shiny interface.
Skills you'll gain
Details to know
April 2026
7 assignments
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There are 6 modules in this course
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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. Data science is transforming how organizations analyze information, build intelligent systems, and create interactive data applications. In this course, you will gain hands-on experience using Python tools such as PyTorch, Pandas-style data workflows, and Shiny for Python to build powerful data-driven applications. You will learn how to visualize data, create dashboards, and implement machine learning workflows using modern data science tools and libraries. The course begins by introducing interactive data applications using Shiny. You will learn how to design responsive user interfaces, implement inputs and outputs, and deploy interactive apps directly from development environments like VSCode. Through guided demonstrations and official Shiny examples, you will understand how real-world dashboards and analytical tools are built for data exploration. Next, the course walks you through building a complete CSV data dashboard. You will implement file uploads, compute quick statistics, and create dynamic visualizations such as histograms, bar charts, and pie charts. By the end of this section, you will understand how to transform raw data into interactive visual insights. In the final modules, you will explore PyTorch fundamentals, including tensors, broadcasting, indexing, GPU acceleration, and tensor operations. You will then apply these skills to build a real-world image classification application using PyTorch and TorchVision integrated with a Shiny interface. This course is designed for aspiring data scientists, Python developers, and analytics professionals who want practical experience building data applications and machine learning systems. Basic knowledge of Python programming and data handling concepts is recommended, and the course is suitable for learners at an intermediate level. By the end of the course, you will be able to build interactive data dashboards, manipulate and analyze datasets, implement PyTorch tensor operations, and deploy machine learning–powered applications using Python and Shiny.
In this module, we will introduce the course structure and learning journey you’ll follow throughout the program. You’ll explore how tools like PyTorch, Python, and interactive dashboards fit into modern data science workflows. By the end, you’ll clearly understand what to expect and how the upcoming modules will build your practical skills.
What's included
1 video
1 video
- Introduction•0 minutes
In this module, we will explore the fundamentals of building interactive data visualization apps using Shiny. You’ll learn how to design user interfaces, connect inputs to server logic, and create dynamic components. Through hands-on examples, you’ll build and deploy functional Shiny apps while understanding the framework’s core capabilities.
What's included
9 videos1 assignment
9 videos•Total 73 minutes
- Input Sliders, Text Output with Simple Server Logic•6 minutes
- Shiny Input Demo•14 minutes
- Using HTML to Build a Multiplication Table in Shiny (Part 1)•4 minutes
- Using HTML to Build a Multiplication Table in Shiny (Part 2)•9 minutes
- Using Shiny in VSCode and Deploying Your App•8 minutes
- Exploring Shiny Components•12 minutes
- Working with Action Buttons and Checkboxes•5 minutes
- Using Checkbox Groups, Selectize, and Row-Column Structures•10 minutes
- Introduction to Shiny Express for Python•5 minutes
1 assignment•Total 15 minutes
- Data Visualization and Shiny - Assessment•15 minutes
In this module, we will explore official Shiny demo projects to better understand real-world application design. You’ll walk through examples such as sidebar layouts, KDE visualizations, and dashboard implementations. These demos will help you analyze best practices and gain inspiration for building your own interactive data applications.
What's included
3 videos1 assignment
3 videos•Total 14 minutes
- Using Official Shiny Demos as a Learning Tool - Sidebar App•7 minutes
- Walkthrough: Shiny's KDE Plot Demo Project•2 minutes
- Walkthrough- Penguins Dashboard Demo by the Shiny Team•6 minutes
1 assignment•Total 15 minutes
- Using Official Shiny Demos as a Learning Tool - Assessment•15 minutes
In this module, we will build a fully functional interactive CSV data dashboard using Shiny for Python. You’ll implement file uploads, dynamic column selection, and summary statistics for real-time data exploration. By the end, you’ll create visualizations and dashboard components that allow users to analyze datasets interactively.
What's included
9 videos1 assignment
9 videos•Total 32 minutes
- Project Setup•2 minutes
- Adding the Imports•2 minutes
- Uploading a CSV File•3 minutes
- Displaying Quick Stats•5 minutes
- Dynamic Column Picker for CSV Data•2 minutes
- Displaying Column Details in an Info Card•5 minutes
- Visualizing Numeric Columns with Histograms•5 minutes
- Visualizing Categorical Columns with Pie or Bar Charts•4 minutes
- Conditional Pie or Bar Charts and No-Data Messaging•6 minutes
1 assignment•Total 15 minutes
- Building an Interactive CSV Data Dashboard in Shiny for Python - Assessment•15 minutes
In this module, we will introduce the core foundations of PyTorch and tensor-based computation. You’ll explore tensor operations, indexing, masking, cloning, and broadcasting through practical coding examples. The lessons will also demonstrate how to leverage GPUs and development tools to accelerate machine learning workflows.
What's included
13 videos1 assignment
13 videos•Total 96 minutes
- Google Colab and tqdm•4 minutes
- How to Get Help with PyTorch•5 minutes
- Exploring Additional Help Resources•4 minutes
- Introduction to PyTorch and Tensors (Part 1)•10 minutes
- Introduction to PyTorch and Tensors (Part 2)•9 minutes
- Leveraging the GPU for PyTorch in Google Colab•2 minutes
- Understanding Mathematical Operations on Tensors•13 minutes
- Understanding Indexing and Masking in Tensors•14 minutes
- Expanding on Masking in PyTorch•14 minutes
- Cloning Tensors for Safe Operations•4 minutes
- Broadcasting in PyTorch: The First Steps•5 minutes
- Broadcasting: Next Steps•8 minutes
- Hands-on with More Broadcasting Examples•4 minutes
1 assignment•Total 15 minutes
- PyTorch Fundamentals - Assessment•15 minutes
In this module, we will build TorchSight, an interactive image classification application powered by PyTorch. You’ll integrate TorchVision models, apply image transformations, and prepare images for neural network inference. By the end, you’ll create a complete Shiny-based interface that allows users to upload images and view classification results instantly.
What's included
8 videos3 assignments
8 videos•Total 29 minutes
- Getting Started with TorchSight•4 minutes
- Adding the PyTorch and Image Processing Imports•3 minutes
- Importing the TorchVision Models•1 minute
- Implementing the Get Model Function•3 minutes
- Image Transformations•3 minutes
- Creating the Title and Sidebar•4 minutes
- Getting the ImageNet Labels and Prompting the User for Images•5 minutes
- PyTorch Inference•5 minutes
3 assignments•Total 90 minutes
- Torch Sight - PyTorch Image Classification using Python and Shiny - Assessment•15 minutes
- Full Course Assessment•60 minutes
- Full Course Practice Assessment•15 minutes
Instructor
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Frequently asked questions
Data Science with PyTorch and Pandas involves using Python tools and libraries to analyze data, build machine learning models, and create interactive data applications. PyTorch is widely used for deep learning and neural network development, while Pandas is essential for data manipulation and analysis. Together, these tools allow developers and analysts to transform raw data into insights and intelligent systems. This skill set is highly relevant today because organizations rely heavily on data-driven decision-making and AI-powered applications across industries.
This course provides a practical introduction to data science using PyTorch, Pandas, and Shiny for Python. You will learn how to visualize and analyze data, build interactive dashboards, and work with machine learning fundamentals using PyTorch. The course walks through building real-world projects such as an interactive CSV data dashboard and an image classification application using pre-trained models. Through hands-on examples, you will explore data visualization techniques, tensor operations, and model inference while learning how to deploy interactive apps.
After completing this course, you will be able to analyze and manipulate datasets using Python tools, build interactive data dashboards with Shiny, and implement basic machine learning workflows with PyTorch. You will understand how tensors work, perform mathematical operations for machine learning tasks, and use GPU acceleration in environments like Google Colab. Additionally, you will gain experience creating real applications such as dashboards that visualize CSV data and an image classification app powered by pre-trained neural networks.
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