Data Visualization Using Python
From network graphs and NLP to Streamlit dashboards and enterprise-grade Dash applications — the visualization skills that turn raw data into decisions that stick.
Self-paced - Mentor-led - 5 weeks
What This Course Is?
You have learned to load, clean, join, and analyse data in Python. Now comes the part that gets you hired and gets your work acted on: making the data visible. Charts are not decoration. They are the language that decision-makers speak. A finding buried in a DataFrame changes nothing; the same finding rendered as a clear, compelling visual changes decisions.
This course takes you beyond bar charts — and beyond generic datasets. You will work with real financial news and market data: scraping, mining text, mapping relationships between companies and topics, and building dashboards that track how markets move. The context is the investment research analyst — someone who uses Python to do what expensive terminals do, independently and for free.
You will learn to map entity relationships using network graphs, extract structure from financial news using natural language processing, build interactive investment dashboards with Streamlit that deploy in minutes, and step up to production-grade Dash applications built for enterprise scale.
Three modules. Nineteen lessons. One portfolio deliverable per module. By the end, you will have three professional pieces of work: a network visualization of financial news, a deployed investment dashboard, and a Dash application built to enterprise specification.
⚠ Prerequisite: This course assumes you have completed Python for Data Analytics. You should be comfortable loading and wrangling DataFrames with pandas, running notebooks in Google Colab, and using the standard Lumen folder structure. If you are not, complete a python for analytics course first.
ⓘ A note on financial data: This course uses real market data and financial news for analytical and educational purposes only. Nothing here constitutes financial advice or investment recommendations. The goal is analytical skill — not to tell you what to buy or sell.
What You Will Build Across All Three Modules
📊 Technical Skills
feedparser, NetworkX, NLTK, spaCy, yfinance, Plotly, Streamlit, Dash, Folium, Matplotlib, Seaborn — the full stack.
📈 Business Acumen
Reading market data. Choosing the right visualization for the right question. Designing for non-technical audiences. Knowing when to prototype fast and when to build for production.
🤖 AI Literacy
Using Gemini to accelerate layout decisions, debug visualization code, and generate stakeholder narrative from chart data — while keeping your own judgment in the driver’s seat.
🌟 Personal Branding
Three portfolio-ready deliverables. GitHub-published. LinkedIn-shareable. Employer-visible.
Module 1: Market Intelligence with NLP and Network Analysis
🕑 8 Lessons · ~24 hours · Deliverable: Interactive network visualization of financial news
Language is data. Every earnings announcement, every analyst report, every market news headline is a dataset waiting to be mapped. In Module 1, you become an investment research analyst — setting up a professional workspace, scraping financial news, mining text for meaning, pulling live price data, and rendering the relationships between companies, topics, and events as an interactive network graph.
You pick a market sector that interests you in Lesson 1 and use it throughout the module. The demo walkthroughs use the AI and tech sector. Your exercises use your own. By the end, you have an original piece of analysis — not a reproduction of someone else’s tutorial.
This is also where you meet Gemini as a coding copilot — embedded from Lesson 3 onward and present in every lesson that follows.
The Investment Research Analyst and Python Tools
What an investment research analyst does — and how Python replaces the expensive terminal. The module project, the tools you’ll use, and your first decision: choosing the sector you’ll analyse.
Setting Up the Python Workspace
Folder structure, Google Drive organisation, and project conventions that keep your work reproducible and shareable. Mounting Drive and setting path variables — the standard opening for every notebook in this course.
Setting Up Your Colab Workspace and AI Copilot
Installing and managing packages in Colab. Pinning versions with requirements.txt. Introducing the Gemini sidebar — how to prompt it, what it gets right, and where to verify before you apply.
Accessing Web Data with Data Scraping
APIs, RSS feeds, and scraping — when to use each. Using feedparser and BeautifulSoup to pull financial news. The legal and ethical boundaries every analyst must understand before scraping anything.
Text Mining Financial News
Tokenising, cleaning, and counting a financial news corpus at scale. Frequency analysis, stopword removal, and term extraction — turning unstructured headlines into structured data.
Intro to NLP and Network Analysis
Named entity recognition to extract companies, people, and topics. Sentiment analysis. Co-occurrence as the basis for network graphs. Nodes, edges, and weights — the conceptual foundation before you build.
Creating Network Visualizations
Building and rendering graphs with NetworkX and Pyvis. Layout algorithms, node sizing by frequency, edge weights by co-occurrence, community colouring — making the invisible structure of financial news visible.
Portfolio and Branding
Packaging the Module 1 project for employers and clients. Publishing to GitHub. Writing about your analysis for a financial or business audience — not a technical one.
📄 Module 1 Deliverable: A GitHub-published network visualization showing how companies, topics, and events relate in your chosen sector — built from a scraped and cleaned financial news corpus, with an annotated Colab notebook and a LinkedIn post describing what you found and why it matters.
Module 2: Investment Dashboards with Streamlit
🕑 8 Lessons · ~24 hours · Deliverable: Deployed live investment dashboard
A chart answers a question. A dashboard answers every question a stakeholder might walk in with. In Module 2, you design and build a fully interactive investment dashboard using Streamlit — Python’s fastest route from analysis to a deployed, shareable web application. No front-end knowledge required. A live URL to show employers and clients within hours, not weeks.
You source live stock data using yfinance — price, volume, and fundamentals including P/E ratio, market cap, and earnings — and apply the full Python visualization library stack to present it. The final lesson covers the skill that separates junior from senior analysts: knowing how to present your work to a room that did not build it.
Tools for Creating Dashboards
The Python dashboard landscape: Streamlit, Dash, Panel, Voilà. When to use each. Why we build with Streamlit in this module — and when Dash is the right answer instead.
Project Planning and Sourcing Data with yfinance
Scoping a dashboard project before writing code. Using yfinance to pull stock price, volume, and fundamentals — P/E ratio, market cap, earnings — and structure it for a dashboard.
Fundamentals of Visualization Libraries — Part 1
Matplotlib and Seaborn in depth. Figure anatomy, subplots, publication-quality styling, and when static charts are the right choice for investment reporting.
Fundamentals of Visualization Libraries — Part 2
Plotly Express and Plotly Graph Objects. Candlestick charts, hover data, subplots, and animations — the building blocks of every interactive dashboard.
Advanced Geospatial Plotting
Choropleth and point maps with Folium and Plotly. Market presence, revenue by country, headquarter locations — when a map is the only chart that tells the story.
Building Dashboards with Streamlit
A multi-chart Streamlit investment dashboard from scratch. Ticker dropdowns, date pickers, metric selectors, and live chart updates — connecting widgets to real market data.
Refining and Deploying a Streamlit Dashboard
The gap between a working dashboard and a presentation-ready one. Colour, labelling, caching for performance, and one-click deployment to Streamlit Community Cloud. Your live URL.
Portfolio and Branding
Your live dashboard URL is your business card. Writing a case study — problem, data, methodology, insight — for a business or investment audience, not a technical one.
📄 Module 2 Deliverable: A fully interactive investment dashboard, deployed and publicly accessible via Streamlit Community Cloud. Live yfinance-sourced data including price, volume, and fundamentals. At least one geospatial visualization. Annotated Colab notebooks. One LinkedIn case study post written for a business, not a technical, audience.
Module 3: Enterprise Investment Dashboards with Dash
🕑 3 Lessons · ~9 hours · Deliverable: Production-grade Dash investment application
Streamlit gets your dashboard in front of stakeholders fast. But some projects demand more: precise layout control, complex interactivity, and a codebase that a team can maintain at scale. That is where Dash earns its place.
Module 3 is built for analysts who want to move beyond prototyping into production. You already have a working Streamlit investment dashboard. Now you rebuild it in Dash — understanding exactly what you gain, what it costs, and when the upgrade is worth recommending to a client. This is the module that bridges the gap between data analyst and data engineer.
From Streamlit to Dash — Why the Upgrade Matters
A direct architectural comparison. When Streamlit’s script model hits its limits. The callback model explained without jargon. Scoping a Dash project from a Streamlit baseline.
Dash Layout, Callbacks, and Interactivity
Building a multi-component Dash investment application. app.layout, @app.callback, Input/Output chaining. The precise, responsive interactivity that enterprise stakeholders expect.
Deploying and Presenting a Dash Dashboard
Deployment options: Render, Railway, self-hosted. Structuring a Dash app for a team codebase. Presenting the finished investment application live — to a non-technical audience, under pressure.
📄 Module 3 Deliverable: A production-grade Dash investment application rebuilt from your Module 2 Streamlit baseline. Deployed and publicly accessible. Annotated code structured for team readability. A one-page written comparison of Streamlit vs Dash, framed for a client audience.
📚 Note: Module 3 can be taken as a standalone upgrade by analysts who already know Streamlit. No prior completion of Modules 1 or 2 required — only comfort with Python, pandas, and Plotly.
Tools and Libraries You Will Use
Every library below is introduced in context — tied to a specific analytical task, not taught in isolation. You will know what each tool does and, crucially, when to reach for it.
| Library | Purpose | Module |
|---|---|---|
| feedparser | Parsing Yahoo Finance RSS feeds for financial news | Module 1 |
| requests / BeautifulSoup | Web scraping article text and HTTP requests | Module 1 |
| NLTK / spaCy | Text mining, NLP, named entity recognition | Module 1 |
| NetworkX / Pyvis | Graph construction and interactive network visualization | Module 1 |
| yfinance | Stock price & volume (Module 1); fundamentals — P/E, market cap, earnings (Module 2) | Modules 1 & 2 |
| Matplotlib / Seaborn | Static charts, statistical plots, publication-quality figures | Module 2 |
| Plotly | Interactive charts, candlesticks, subplots, animations, geographic plots | Modules 2 & 3 |
| Streamlit | Rapid dashboard prototyping, deployment, and sharing | Module 2 |
| Folium | Interactive geospatial maps (Leaflet-based) | Module 2 |
| Dash | Enterprise dashboard framework with callbacks and full layout control | Module 3 |
| Google Colab + Gemini | Execution environment and AI copilot throughout | All modules |
🤖 Gemini Copilot — Throughout All Three Modules
Gemini is threaded into every lesson as an analytical accelerator, not a shortcut. You will use it to debug visualization code, suggest layout improvements, generate first-draft stakeholder narratives, and critically evaluate AI output against your own domain knowledge. The goal is not to have Gemini do the work. The goal is to move faster without losing judgment.
Who This Course Is For
Career changers
Who need portfolio pieces that demonstrate Python visualization skills at a professional level — not tutorial screenshots.
Analytics professionals
Who want to move beyond Excel charts and static slides to dynamic, self-service visualizations that run in a browser.
Freelance analysts
Who want to add network analysis and interactive dashboards to their client offer — services that command premium rates.
What You Leave With
- A network visualization of financial news entities in your chosen sector - GitHub-published, portfolio-ready
- A deployed Streamlit investment dashboard with a live public URL - shareable in a job application or client proposal today
- A production-grade Dash application demonstrating enterprise-level Python dashboard skills
- Hands-on experience with eleven Python libraries across the full visualization and financial data stack
- The ability to work with live financial data, scraped news corpora, and geospatial data - not just CSV files
- Three portfolio deliverables and the LinkedIn presence to make sure the right people see them
Program Details
Everything you need to know before you enrol.
| Key Info | What you need to know |
|---|---|
| Duration | 5 weeks |
| Format | Online - self-paced with mentor support. 5 mentor calls + unlimited async support. |
| Price | €499 (+ 19% VAT in EU) |
| Prerequisites | Python, and data analysis fundamentals. |
| Seats | Limited. Application only. |
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Have questions? We’re here to help! Whether you’re curious to learn more, want guidance on applying, or need insights to make the right decision—reach out today and take the first step toward transforming your career.