Hands-On Data Science with PyTorch & Pandas

Hands-On Data Science with PyTorch & Pandas

位教师：Packt - Course Instructors

访问权限由 New York State Department of Labor 提供

6个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

6个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

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要了解的详细信息

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作业

7 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有6个模块

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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.

涵盖的内容

1个视频

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.

涵盖的内容

9个视频1个作业

9个视频总计73分钟

Input Sliders, Text Output with Simple Server Logic6分钟
Shiny Input Demo14分钟
Using HTML to Build a Multiplication Table in Shiny (Part 1)4分钟
Using HTML to Build a Multiplication Table in Shiny (Part 2)9分钟
Using Shiny in VSCode and Deploying Your App8分钟
Exploring Shiny Components12分钟
Working with Action Buttons and Checkboxes5分钟
Using Checkbox Groups, Selectize, and Row-Column Structures10分钟
Introduction to Shiny Express for Python5分钟

1个作业总计15分钟

Data Visualization and Shiny - Assessment15分钟

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.

涵盖的内容

3个视频1个作业

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.

涵盖的内容

9个视频1个作业

9个视频总计32分钟

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

1个作业总计15分钟

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

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.

涵盖的内容

13个视频1个作业

13个视频总计96分钟

Google Colab and tqdm4分钟
How to Get Help with PyTorch5分钟
Exploring Additional Help Resources4分钟
Introduction to PyTorch and Tensors (Part 1)10分钟
Introduction to PyTorch and Tensors (Part 2)9分钟
Leveraging the GPU for PyTorch in Google Colab2分钟
Understanding Mathematical Operations on Tensors13分钟
Understanding Indexing and Masking in Tensors14分钟
Expanding on Masking in PyTorch14分钟
Cloning Tensors for Safe Operations4分钟
Broadcasting in PyTorch: The First Steps5分钟
Broadcasting: Next Steps8分钟
Hands-on with More Broadcasting Examples4分钟

1个作业总计15分钟

PyTorch Fundamentals - Assessment15分钟

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.