Overview

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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. 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.

Syllabus

Introduction

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.

Data Visualization and Shiny

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.

Using Official Shiny Demos as a Learning Tool

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.

Building an Interactive CSV Data Dashboard in Shiny for Python

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.

PyTorch Fundamentals

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.

Torch Sight - PyTorch Image Classification using Python and Shiny

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.