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Advanced Data Visualization

Discover how to visualize time, proportions, relationships, and spatial patterns with the right tools and design. 

(data-visualize) / ISBN : 978-1-61691-723-4
Lessons
Lab
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About This Course

In a world flooded with dashboards, charts, and spreadsheets, the ability to tell clear, impactful stories with data has never been more critical.

In this Advanced Data Visualization course, take a deep dive into techniques such as dual-axis charts for revealing hidden correlations, multi-chart dashboards for holistic views, and complex network graphs to map out your intricate relationships. 

You’ll explore how to choose the right tools and techniques for every story. So your audience can see what matters most, avoid misinterpretation, and engage meaningfully with your work. 

Skills You’ll Get

  • Geospatial Mapping: Master the creation of choropleth maps, heatmaps & not destiny maps to visualize your geographical distributions & patterns. 
  • Statistical Visualization: Explore advanced statistical plots, including scatter plot matrices, box-and-whisker plots, and violin plots, to understand data distribution and relationships. 
  • Data Presentation: Create compelling executive dashboards that distill complex information into clear, & concise insights. 
  • Data Storytelling: Craft compelling visual narratives that highlight trends, patterns, and key insights. 
  • Data Cleaning and Preparation: Scrap, format, and prepare data to design & implement actionable business reports.
Download Course Outline

1

Getting Started

2

Telling Stories with Data

  • 1.0 Competency One and Objectives
  • Video: The Art of Data Visualization
  • More Than Numbers
  • What to Look For
  • Design
  • Wrapping Up
3

Handling Data

  • 2.0 Competency Two and Objectives
  • Gather Data
  • Formatting Data
  • Wrapping Up
4

Choosing Tools to Visualize Data

  • Out-of-the-Box Visualization
  • Programming
  • Illustration
  • Mapping
  • Survey Your Options
  • Wrapping Up
5

Visualizing Patterns over Time

  • 4.0 Competency Three and Objectives
  • What to Look for over Time
  • Discrete Points in Time
  • Continuous Data
  • Wrapping Up
6

Visualizing Proportions

  • What to Look for in Proportions
  • Parts of a Whole
  • Proportions over Time
  • Wrapping Up
7

Visualizing Relationships

  • 6.0 Competency Four and Objectives
  • What Relationships to Look For
  • Correlation
  • Distribution
  • Comparison
  • Wrapping Up
8

Spotting Differences

  • What to Look For
  • Comparing across Multiple Variables
  • Reducing Dimensions
  • Searching for Outliers
  • Wrapping Up
9

Visualizing Spatial Relationships

  • What to Look For
  • Specific Locations
  • Regions
  • Over Space and Time
  • Wrapping Up
10

Designing with a Purpose

  • 9.0 Competency Five and Objectives
  • Prepare Yourself
  • Prepare Your Readers
  • Visual Cues
  • Good Visualization
  • Wrapping Up
A

Appendix

12

Tutorials

1

Handling Data

  • Scrape, format and present your data
  • Clean and Format Data using Excel
  • Changing Formats using Python

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An advanced data visualization course is suitable for analysts, researchers, educators, data professionals, and anyone who wants to communicate data clearly and effectively. 

Advanced data visualization refers to the use of sophisticated techniques and tools to simply represent complex data in an easy-to-understand visual format. It goes beyond the basic charts and graphs by helping users quickly grasp trends, patterns, and relationships within data. Ultimately, it leads to better decision-making. 

The data advanced visualization online course can offer multiple career benefits, including career advancements, increased earning potential & new job opportunities.  

Both are powerful:

  • R excels in statistical plots and quick visualizations with libraries like ggplot2.
  • Python offers greater flexibility and integration with tools like Matplotlib, Seaborn, and Plotly. 

Tableau remains one of the most in-demand tools in the industry, followed closely by Power BI for business analytics and Python libraries like Plotly and Seaborn in data science roles. 

AI platforms like ChatGPT with code interpreters, Tableau GPT, and Power BI’s Copilot are leading the way, offering smart suggestions, auto-generated visuals, and natural language data queries to enhance visualization and insight discovery. 

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