Here’s a 15-week teaching schedule for Data Visualization using Python with three lectures per week.
Course Title: Data Visualization using Python
Duration: 15 Weeks (3 lectures per week)
Total Lectures: 45
Target Audience: Beginners to Intermediate learners
Prerequisites: Basic Python knowledge and familiarity with Pandas
Week 1: Introduction to Data Visualization
Lecture 1: Importance of Data Visualization & Applications
Lecture 2: Introduction to Matplotlib and Seaborn
Lecture 3: Understanding Different Chart Types
Week 2: Matplotlib Basics
Lecture 4: Line Plots, Bar Charts, and Histograms
Lecture 5: Customizing Plots (Colors, Markers, Labels, Titles)
Lecture 6: Subplots and Multiple Plots
Week 3: Advanced Matplotlib Techniques
Lecture 7: Customizing Axes, Legends, and Grids
Lecture 8: Annotations and Text in Plots
Lecture 9: 3D Plotting with Matplotlib
Week 4: Introduction to Seaborn
Lecture 10: Seaborn vs Matplotlib – When to Use Each?
Lecture 11: Basic Plots in Seaborn (Barplot, Countplot, Boxplot)
Lecture 12: Styling Seaborn Plots
Week 5: Statistical Data Visualization with Seaborn
Lecture 13: Distribution Plots (Histplot, KDEplot, Violinplot)
Lecture 14: Bivariate Analysis (Scatterplot, Pairplot, Jointplot)
Lecture 15: Correlation and Heatmaps
Week 6: Pandas Built-in Visualization
Lecture 16: Quick Data Visualizations using Pandas
Lecture 17: Plotting Time Series Data with Pandas
Lecture 18: Customizing Pandas Plots
Week 7: Interactive Visualizations with Plotly
Lecture 19: Introduction to Plotly and Dash
Lecture 20: Creating Interactive Line, Bar, and Scatter Plots
Lecture 21: Customizing Interactivity in Plotly
Week 8: Advanced Plotly Visualizations
Lecture 22: 3D Plots and Surface Plots
Lecture 23: Geo-spatial Visualizations using Plotly
Lecture 24: Dashboards using Plotly Dash
Week 9: Data Storytelling and Dashboard Design
Lecture 25: Fundamentals of Data Storytelling
Lecture 26: Designing Effective Dashboards
Lecture 27: Case Study: Analyzing Business Data
Week 10: Specialized Plots for Business & Finance
Lecture 28: Candlestick Charts for Financial Data
Lecture 29: Waterfall Charts and Funnel Charts
Lecture 30: Sunburst and Treemap Visualizations
Week 11: Geographic Data Visualization
Lecture 31: Basics of Geo-spatial Data
Lecture 32: Creating Choropleth Maps with Folium
Lecture 33: Advanced Geospatial Analysis
Week 12: Advanced Data Visualization Techniques
Lecture 34: Network Graphs using NetworkX
Lecture 35: Sankey Diagrams and Word Clouds
Lecture 36: Animated Visualizations with Matplotlib & Plotly
Week 13: Automation and Report Generation
Lecture 37: Automating Report Generation in Python
Lecture 38: Exporting Visualizations to PDF, Excel, and PPT
Lecture 39: Deploying Interactive Dashboards
Week 14: Real-World Projects and Case Studies
Lecture 40: Sales Data Analysis Dashboard
Lecture 41: COVID-19 Data Visualization Case Study
Lecture 42: Social Media Trends Analysis
Week 15: Capstone Project & Career Guidance
Lecture 43: Capstone Project Development
Lecture 44: Project Presentations & Feedback
Lecture 45: Career Guidance – Resume, Portfolio, and Interview Tips
Final Deliverables:
One Capstone Project
Hands-on Assignments for each module
Portfolio of Data Visualizations
Would you like me to tailor the schedule for a specific audience, such as business professionals or students?
Here’s a revised 15-week schedule with a stronger emphasis on hands-on projects, labs, and applied topics for a Data Visualization using Python course. Each week includes coding labs, mini-projects, and real-world datasets to ensure practical skill-building:
Week 1: Python & Data Prep Bootcamp
- Lecture 1: Environment Setup & Python Refresher
- Lab: Install Anaconda, Jupyter, VS Code. Code basics (lists, loops, functions).
- Dataset: Simple CSV (e.g., Titanic survival data).
- Lecture 2: NumPy & Pandas Crash Course
- Lab: Clean and analyze a messy dataset (e.g., missing values, duplicates).
- Lecture 3: Data Import/Export & APIs
- Mini-Project: Fetch real-time data from an API (e.g., OpenWeatherMap) and store it in a DataFrame.
Week 2: Matplotlib Deep Dive
- Lecture 1: Basic Plots (Line, Bar, Scatter)
- Lab: Visualize stock price trends (e.g., Apple stock data).
- Lecture 2: Customizing Plots
- Lab: Design publication-quality charts (e.g., add annotations, dual axes).
- Lecture 3: Subplots & Advanced Charts
- Mini-Project: Create a dashboard of COVID-19 trends using subplots.
Week 3: Seaborn for Statistical Insights
- Lecture 1: Distribution & Relationship Plots
- Lab: Analyze the “diamonds” dataset (e.g., carat vs. price).
- Lecture 2: Categorical & Regression Plots
- Lab: Compare GDP growth across continents with violin plots.
- Lecture 3: FacetGrid & PairGrid
- Mini-Project: Build a correlation matrix for housing data with heatmaps.
Week 4: Interactive Visuals with Plotly
- Lecture 1: Plotly Express Basics
- Lab: Create an animated bubble chart (e.g., global population over time).
- Lecture 2: Custom Interactivity
- Lab: Add dropdowns to compare COVID-19 metrics across countries.
- Lecture 3: Plotly Dashboards
- Mini-Project: Build an interactive dashboard for Airbnb listings.
Week 5: Geospatial Mapping
- Lecture 1: Geopandas & Choropleth Maps
- Lab: Map U.S. election results by state.
- Lecture 2: Interactive Maps with Folium
- Lab: Plot earthquake locations with custom pop-ups.
- Lecture 3: Advanced Geospatial Analysis
- Mini-Project: Visualize Uber ride density in NYC using heatmaps.
Week 6: Time Series & Financial Data
- Lecture 1: Time Series Basics (Matplotlib/Plotly)
- Lab: Plot cryptocurrency price volatility.
- Lecture 2: Candlestick Charts & Trends
- Lab: Analyze stock market data with moving averages.
- Lecture 3: Annotating Events
- Mini-Project: Visualize the impact of news events on stock prices.
Week 7: Dashboarding with Dash
- Lecture 1: Dash Layouts & Callbacks
- Lab: Build a simple dashboard for weather data.
- Lecture 2: Advanced Dash Components
- Lab: Add sliders and live-updating graphs.
- Lecture 3: Multi-Page Apps
- Mini-Project: Create a personal finance tracker dashboard.
Week 8: Visualization for Big Data
- Lecture 1: Datashader Basics
- Lab: Visualize 1 million+ points (e.g., NYC taxi rides).
- Lecture 2: Dask for Parallel Processing
- Lab: Speed up ETL workflows for large datasets.
- Lecture 3: 3D Visualizations
- Mini-Project: Render 3D terrain maps with elevation data.
Week 9: Design & Storytelling
- Lecture 1: Color Theory & Accessibility
- Lab: Redesign a poorly formatted chart using ColorBrewer palettes.
- Lecture 2: Storytelling with Data
- Lab: Craft a narrative around climate change data (CO2 vs. temperature).
- Lecture 3: Critique Workshop
- Mini-Project: Analyze and improve a misleading visualization.
Week 10: Ethics & Final Project Kickoff
- Lecture 1: Ethical Pitfalls in Visualization
- Lab: Case study on misrepresented COVID-19 data.
- Lecture 2: Project Proposals & Dataset Selection
- Lab: Brainstorm ideas (e.g., social media trends, sports analytics).
- Lecture 3: Data Cleaning Sprint
- Lab: Preprocess your project dataset (handle outliers, normalize).
Weeks 11–13: Final Project Development
- Structured Labs:
- Week 11: Exploratory Data Analysis (EDA) & Draft Visualizations.
- Week 12: Iterative Feedback (peer reviews, instructor critiques).
- Week 13: Refinement & Dashboard Integration.
- Tools: GitHub for version control, Plotly/Dash for interactivity.
Week 14: Presentations & Deployment
- Lecture 1: Presentation Skills
- Lab: Rehearse with mock presentations.
- Lectures 2–3: Final Project Demos
- Deliverable: Deploy Dash apps on Heroku/Streamlit Share.
Week 15: Emerging Trends & Wrap-Up
- Lecture 1: AI-Driven Visualization (AutoViz, ChatGPT plugins).
- Lecture 2: Career Applications (Portfolio Building).
- Lecture 3: Course Retrospective & Certificates.
Hands-On Focus
- Weekly Labs: Code-along sessions with provided datasets.
- Mini-Projects: Themed assignments (e.g., finance, climate, sports).
- Final Project: End-to-end workflow (data acquisition → cleaning → storytelling → deployment).
- Tools: GitHub Classroom, Kaggle datasets, real-world APIs.
Let me know if you’d like to tweak specific weeks! 🚀