Interactive Data Science In Python With Shiny And Pytorch

Learn to Build Interactive Data Science Apps Using Python, Shiny, Pandas, Seaborn, Matplotlib & PyTorch

What you'll learn

Build interactive web applications and dashboards using Shiny and Shiny Express in Python to visualize and explore data dynamically.

Master core Python data science libraries — Pandas, Seaborn, and Matplotlib — for effective data cleaning, analysis, and visualization.

Understand fundamental deep learning concepts and implement basic neural networks using PyTorch from scratch.

tch. Apply practical, hands-on techniques to create real-world data-driven projects that combine interactivity with machine learning insights.

Requirements

Basic understanding of Python programming (variables, functions, loops).

No prior experience with Shiny, data visualization libraries, or PyTorch is required — everything is taught from scratch.

A computer with Python installed

Curiosity and willingness to learn interactive data science and pytorch fundamentals.

Description

Unlock the power of interactive data science with Interactive Data Science in Python — a comprehensive, beginner-friendly course designed to take you from novice to confident practitioner. We begin by exploring Shiny, the dynamic and popular web app framework for Python, where you'll learn how to build interactive dashboards, responsive data visualizations, and user-friendly interfaces using the classic Shiny library. Once you’ve gained solid skills, you’ll transition smoothly to Shiny Express, a modern, more streamlined toolkit that accelerates app development while maintaining full flexibility.Alongside Shiny, you’ll dive deep into essential Python data science libraries like Pandas, Seaborn, and Matplotlib. You’ll master how to clean, analyze, visualize, and explore complex datasets with clarity and precision, empowering you to uncover patterns and tell compelling stories with data.This course also introduces PyTorch basics from scratch — perfect for beginners eager to explore deep learning and neural networks. You’ll grasp fundamental machine learning concepts and get hands-on experience building your own models, preparing you to confidently tackle more advanced AI projects.Throughout the course, you’ll engage with practical coding exercises, real-world datasets, and projects focused on creating interactive applications that captivate users and dynamically reveal insights. Whether you aspire to be a data scientist, analyst, or developer, this course will equip you with the skills and confidence to build powerful data-driven applications and understand foundational deep learning techniques in Python.Jump in today and bring your data to life with interactive, intelligent applications!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Data Visualization and Shiny

Lecture 2 Input Sliders, Text Output with Simple Server Logic

Lecture 3 Shiny Input Demo

Lecture 4 Using HTML to Build a Multiplication Table in Shiny (Part 1)

Lecture 5 Using HTML to Build a Multiplication Table in Shiny (Part 2)

Lecture 6 Using Shiny in VSCode and Deploying Your App

Lecture 7 Exploring Shiny Components

Lecture 8 Working with Action Buttons and Checkboxes

Lecture 9 Using Checkbox Groups, Selectize, and Row-Column Structures

Lecture 10 Introduction to Shiny Express for Python

Section 3: Using Official Shiny Demos as a Learning Tool

Lecture 11 Using Official Shiny Demos as a Learning Tool - Sidebar App

Lecture 12 Walkthrough: Shiny’s KDE Plot Demo Project

Lecture 13 Walkthrough- Penguins Dashboard Demo by the Shiny Team

Section 4: Building an Interactive CSV Data Dashboard in Shiny for Python

Lecture 14 Project Setup

Lecture 15 Adding the Imports

Lecture 16 Uploading a CSV File

Lecture 17 Displaying Quick Stats

Lecture 18 Dynamic Column Picker for CSV Data

Lecture 19 Displaying Column Details in an Info Card

Lecture 20 Visualizing Numeric Columns with Histograms

Lecture 21 Visualizing Categorical Columns with Pie or Bar Charts

Lecture 22 Conditional Pie or Bar Charts and No-Data Messaging

Section 5: PyTorch Fundamentals

Lecture 23 Google Colab and tqdm

Lecture 24 How to Get Help with PyTorch

Lecture 25 Exploring Additional Help Resources

Lecture 26 Introduction to PyTorch and Tensors (Part 1)

Lecture 27 Introduction to PyTorch and Tensors (Part 2)

Lecture 28 Leveraging the GPU for PyTorch in Google Colab

Lecture 29 Understanding Mathematical Operations on Tensors

Lecture 30 Understanding Indexing and Masking in Tensors

Lecture 31 Expanding on Masking in PyTorch

Lecture 32 Cloning Tensors for Safe Operations

Lecture 33 Broadcasting in PyTorch: The First Steps

Lecture 34 Broadcasting: Next Steps

Lecture 35 Hands-on with More Broadcasting Examples

Section 6: Torch Sight - PyTorch Image Classification using Python and Shiny

Lecture 36 Getting Started with TorchSight

Lecture 37 Adding the PyTorch and Image Processing Imports

Lecture 38 Importing the TorchVision Models

Lecture 39 Implementing the Get Model Function

Lecture 40 Image Transformations

Lecture 41 Creating the Title and Sidebar

Lecture 42 Getting the ImageNet Labels and Prompting the User for Images

Lecture 43 PyTorch Inference

Those who prefer learning through building interactive applications rather than theory alone.,Aspiring data scientists, analysts, and developers looking to build dynamic dashboards and web apps with Shiny.,Anyone interested in learning PyTorch basics,Beginners and intermediate Python users who want to dive into interactive data science and visualization.