Course Outline

• Introduction to Data Visualization: Overview of data visualization principles, importance of visualizing data, and introduction to popular visualization tools.
• Understanding Data Structures: Exploring different data structures including arrays, lists, and dictionaries, and their role in data visualization.
• Basic Visualization Techniques: Introduction to basic visualization techniques such as bar charts, line charts, and scatter plots.
• Statistical Foundations for Data Science: Fundamentals of statistics including probability, distributions, and hypothesis testing.
• Advanced Visualization Methods: Dive into advanced visualization methods including heatmaps, treemaps, and geospatial visualization.
• Data Cleaning and Preprocessing: Techniques for cleaning and preprocessing data before visualization, handling missing values, outliers, and duplicates.
• Interactive Visualizations: Creating interactive visualizations using libraries like Plotly and Bokeh to enhance user engagement.
• Dashboard Creation: Building dynamic dashboards to present multiple visualizations and insights in a cohesive manner.
• Customizing Visualizations: Techniques for customizing visualizations with different colors, fonts, and styles to improve readability and aesthetics.
• Storytelling with Data: Strategies for effectively telling stories with data, including narrative structure, data-driven storytelling, and visual storytelling techniques.
• Real-World Applications: Applying data visualization techniques to real-world datasets and case studies in various domains such as finance, healthcare, and marketing.
• Data Visualization Best Practices: Guidelines and best practices for creating effective and impactful visualizations, including accessibility, simplicity, and accuracy.
  
• Data Visualization Tools and Platforms: Overview of popular data visualization tools and platforms such as Tableau, Power BI, and D3.js.