Welcome to the Data Visualization class. This pages outlines the structure and content for our 15-week journey together. Each week focuses on a distinct aspect of data visualization, providing a comprehensive understanding of the field.
Summary:
- Introduction to the course, syllabus, and key tools for communication.
- Focus on collecting and visualizing data using Google Forms and Google Sheets.
- Introduction to GitHub and Markdown for documentation.
Summary:
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Google Colab: Get acquainted with Google Colab as a powerful cloud-based platform for writing and executing Python code. Learn how to create and manage notebooks for data analysis and visualization.
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Python Basics: Introduction to fundamental Python concepts, including variables, data types, loops, and basic data structures. Start writing simple Python scripts to manipulate and analyze data.
Summary:
- Summary: Introduction to Matplotlib for creating line and scatter plots in Python. Learn to customize plots with different styles, colors, and markers, and visualize various mathematical functions using these fundamental plotting techniques.
Summary:
Week Focus: Using bar graphs to compare numerical values across categories.
- Bar Graph Types:
- Simple Bar Charts: Individual amounts; start y-axis at zero.
- Grouped Bar Charts: Compare values within a category.
- Stacked Bar Charts: Show parts of a whole.
Summary:
Week Focus: Visualizing distributions I using histograms and Kernel Density Estimation (KDE).
- Plot Types:
- Histograms: Represent frequency of data within bins; adjust bin width for better clarity.
- Kernel Density Estimation (KDE): Smooths data to show continuous distribution trends.
- Stacked Histograms: Display how parts of a dataset contribute to the whole, stacked across different categories.
- Combined KDE: Overlay multiple KDE plots to compare distributions for different categories.
- Combined Histograms: Overlay multiple histograms with transparency to compare distributions across categories (e.g., male vs. female).
For more detailed answers to common questions about the course, check out the full Q&A section. This resource covers everything from course materials and tools to tips for succeeding in Data Science.
Note:
To review material from last year, please check this repository:
Data110-32213 Repository