Python Dashboard Course

Lesson 1 • Dashboard Design Principles

Introduces layout hierarchy, visual encoding, and audience-centered design. Connects design theory to practical decisions made throughout the course.

Lesson 2 • Setting Up the Python Environment

Install Python, configure virtual environments, and manage dependencies with pip. Establishes the reproducible workspace required for all subsequent dashboard projects.

Lesson 3 • Core Python Syntax Review

Covers variables, control flow, functions, and list comprehensions at a practical level. Ensures students can write clean, readable scripts before tackling data tasks.

Lesson 1 • Aggregation and GroupBy Operations

Computes grouped summaries, pivot tables, and rolling statistics. Generates the aggregated metrics displayed as KPIs and trend charts in dashboards.

Lesson 2 • Filtering, Sorting, and Slicing

Applies boolean masks, loc/iloc indexing, and multi-column sorts. Enables dynamic data subsetting that mirrors interactive dashboard filter behaviour.

Lesson 3 • Exporting Processed Data

Writes cleaned DataFrames to CSV, Parquet, and SQLite for downstream use. Establishes data persistence patterns that support dashboard data pipelines.

Lesson 4 • Loading and Inspecting Data

Reads CSV, Excel, and JSON files into DataFrames and profiles their structure. Establishes the data-loading patterns reused in every dashboard project.

Lesson 1 • Statistical Charts with Seaborn

Generates heatmaps, box plots, pair plots, and regression plots using Seaborn. Adds statistical context to dashboards with minimal additional code.

Lesson 2 • Composing Multi-Panel Figures

Arranges multiple chart types into cohesive multi-panel layouts using GridSpec. Translates directly to the multi-widget layout design used in interactive dashboards.

Lesson 3 • Styling and Theming Charts

Applies style sheets, custom color palettes, and typography settings. Ensures visual consistency between static mockups and final interactive dashboards.

Lesson 4 • Core Chart Types in Matplotlib

Builds line, bar, scatter, and histogram charts with full axis and label control. Covers the foundational chart vocabulary used across all dashboard frameworks.

Lesson 1 • Subplots and Faceting

Arranges multiple Plotly charts into shared subplot grids and faceted panels. Mirrors the multi-chart layout patterns used in production dashboards.

Lesson 2 • Exporting and Embedding Charts

Exports charts as HTML, PNG, and SVG and embeds them in notebooks and web pages. Prepares students to deliver standalone interactive charts before full dashboard deployment.

Lesson 3 • Plotly Express Quick Start

Creates scatter, bar, line, and map charts with single-line Plotly Express calls. Demonstrates the fastest path from a DataFrame to an interactive chart.

Lesson 4 • Interactivity Features

Configures hover templates, click events, range sliders, and animation frames. Transforms static data into explorable, user-driven visual experiences.

Lesson 1 • Layout Design and Styling

Applies CSS classes, inline styles, and Dash Bootstrap Components for responsive layouts. Produces professional, mobile-friendly dashboard interfaces.

Lesson 2 • Multi-Page Dash Applications

Implements page routing, shared navigation bars, and per-page layouts in Dash. Scales single-page prototypes into full multi-section dashboard applications.

Lesson 3 • Dash Application Structure

Explains the app object, layout tree, and server configuration for a minimal Dash app. Establishes the structural pattern every subsequent Dash project follows.

Lesson 4 • Dash Core and HTML Components

Uses dcc and html modules to add dropdowns, sliders, graphs, and text elements. Builds the interactive UI layer that users see and manipulate in the browser.

Lesson 1 • Data Validation and Error Handling

Applies schema validation, try/except blocks, and fallback datasets to pipeline errors. Prevents broken dashboards when upstream data sources fail or change.

Lesson 2 • Connecting to SQL Databases

Queries relational databases using SQLAlchemy and pandas read_sql. Replaces static CSV files with live database-backed data sources in dashboards.

Lesson 3 • Fetching Data from REST APIs

Uses the requests library to call REST endpoints and parse JSON responses. Enables dashboards to display real-time data from external services.

Lesson 4 • Scheduled Data Refresh

Implements dcc.Interval and APScheduler to trigger periodic data fetches. Keeps dashboard metrics current without requiring manual page reloads.

Lesson 1 • Custom Dash Components

Builds reusable React-based components and packages them for use in Dash apps. Extends the Dash component library to meet unique business UI requirements.

Lesson 2 • User Authentication and Access Control

Implements login flows, session tokens, and role-based view restrictions in Dash. Protects sensitive business data from unauthorised dashboard access.

Lesson 3 • Caching and Memoisation

Applies Flask-Caching and functools.lru_cache to expensive data queries and transforms. Reduces database load and speeds up repeated dashboard interactions.

Lesson 4 • Large Dataset Handling

Uses server-side pagination, data virtualisation, and Dask for out-of-memory datasets. Enables dashboards to remain responsive when data volumes exceed memory limits.

Lesson 1 • Automated Testing for Dashboards

Writes unit tests for callbacks and end-to-end tests using Dash Testing and Selenium. Catches regressions before deployment and enforces dashboard quality standards.

Lesson 2 • Reverse Proxy and HTTPS Setup

Configures Nginx as a reverse proxy with TLS termination in front of the Dash server. Secures dashboard traffic and enables custom domain routing in production.

Lesson 3 • Monitoring and Observability

Instruments dashboards with structured logging, metrics endpoints, and alerting rules. Provides operational visibility into dashboard health and user behaviour in production.

Lesson 4 • Containerising Dash Applications

Packages Dash apps into Docker images with reproducible environments and entry points. Enables consistent deployment across development, staging, and production environments.