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Hands-On Data Science with PyTorch & Pandas

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

Hands-On Data Science with PyTorch & Pandas

Instructeur : Packt - Course Instructors

Inclus avec

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

7 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Image Analysis
Catégorie : Data Visualization
Catégorie : Dashboard Creation
Catégorie : Application Development
Catégorie : User Interface (UI)
Catégorie : Applied Machine Learning
Catégorie : Machine Learning Methods
Catégorie : Data Visualization Software
Catégorie : Interactive Data Visualization
Catégorie : Data Science
Catégorie : Computer Programming

Outils que vous découvrirez

Catégorie : Shiny (R Package)
Catégorie : Dashboard
Catégorie : AI Workflows
Catégorie : Pandas (Python Package)
Catégorie : Web Development Tools
Catégorie : UI Components
Catégorie : PyTorch (Machine Learning Library)
Catégorie : Web Frameworks
Catégorie : Python Programming

Détails à connaître

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avril 2026

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Il y a 6 modules dans ce cours

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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.

Inclus

1 vidéo

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.

Inclus

9 vidéos1 devoir

9 vidéosTotal 73 minutes

Input Sliders, Text Output with Simple Server Logic6 minutes
Shiny Input Demo14 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 App8 minutes
Exploring Shiny Components12 minutes
Working with Action Buttons and Checkboxes5 minutes
Using Checkbox Groups, Selectize, and Row-Column Structures10 minutes
Introduction to Shiny Express for Python5 minutes

1 devoirTotal 15 minutes

Data Visualization and Shiny - Assessment15 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.

Inclus

3 vidéos1 devoir

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.

Inclus

9 vidéos1 devoir

9 vidéosTotal 32 minutes

Project Setup2 minutes
Adding the Imports2 minutes
Uploading a CSV File3 minutes
Displaying Quick Stats5 minutes
Dynamic Column Picker for CSV Data2 minutes
Displaying Column Details in an Info Card5 minutes
Visualizing Numeric Columns with Histograms5 minutes
Visualizing Categorical Columns with Pie or Bar Charts4 minutes
Conditional Pie or Bar Charts and No-Data Messaging6 minutes

1 devoirTotal 15 minutes

Building an Interactive CSV Data Dashboard in Shiny for Python - Assessment15 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.

Inclus

13 vidéos1 devoir

13 vidéosTotal 96 minutes

Google Colab and tqdm4 minutes
How to Get Help with PyTorch5 minutes
Exploring Additional Help Resources4 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 Colab2 minutes
Understanding Mathematical Operations on Tensors13 minutes
Understanding Indexing and Masking in Tensors14 minutes
Expanding on Masking in PyTorch14 minutes
Cloning Tensors for Safe Operations4 minutes
Broadcasting in PyTorch: The First Steps5 minutes
Broadcasting: Next Steps8 minutes
Hands-on with More Broadcasting Examples4 minutes

1 devoirTotal 15 minutes

PyTorch Fundamentals - Assessment15 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.

Inclus

8 vidéos3 devoirs

8 vidéosTotal 29 minutes

Getting Started with TorchSight4 minutes
Adding the PyTorch and Image Processing Imports3 minutes
Importing the TorchVision Models1 minute
Implementing the Get Model Function3 minutes
Image Transformations3 minutes
Creating the Title and Sidebar4 minutes
Getting the ImageNet Labels and Prompting the User for Images5 minutes
PyTorch Inference5 minutes

3 devoirsTotal 90 minutes

Torch Sight - PyTorch Image Classification using Python and Shiny - Assessment15 minutes
Full Course Assessment60 minutes
Full Course Practice Assessment15 minutes

Instructeur

Packt - Course Instructors

Packt

1 749 Cours494 468 apprenants

Offert par

Packt

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Foire Aux 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.

Basic familiarity with Python programming is recommended to get the most out of this course. Understanding simple programming concepts such as variables, functions, and data structures will help you follow the exercises more easily. While prior experience with machine learning or deep learning is not required, some exposure to data analysis concepts can be helpful as the course introduces key tools and techniques used in data science.

This course is designed for aspiring data scientists, machine learning enthusiasts, Python developers, and analysts who want hands-on experience with modern data science tools. It is also suitable for learners who want to explore how interactive applications can be built using data visualization and machine learning frameworks. Anyone interested in combining Python-based data analysis with deep learning and practical projects will benefit from this course.

The course is designed to be completed in approximately four hours. It is structured into multiple short lessons covering data visualization, interactive dashboards, PyTorch fundamentals, and an image classification project. This concise format allows learners to gain practical exposure to key data science concepts in a short amount of time while working through hands-on demonstrations and examples.