Data visualization

1. • • Categorical (Nominal/Ordinal): Nominal (labels, no order, e.g., gender, city) or Ordinal (labels with order, e.g., education level, satisfaction ratings).
     • Temporal (Time-based): Dates, times.
     • Geographical (Spatial): Locations, coordinates.
   • What are the key variables? Identify the columns/fields you want to visualize.
   • What are their ranges and distributions? (e.g., min/max values, averages, outliers).
   • Are there any missing values or inconsistencies? Clean your data if necessary.

2. Define Your Goal and Message:

   • What question are you trying to answer? (e.g., "Which product sells the most?", "How has our revenue changed over time?").
   • What insight do you want to convey? (e.g., "Sales peaked in Q3," "Product A consistently underperforms").
   • Who is your audience? (e.g., executives, technical team, general public). This influences complexity, jargon, and visual style.
   • What action do you want your audience to take? (e.g., "Invest in Product X," "Adjust marketing strategy").

Phase 2: Choose the Right Visualization Type

This is the most critical step. The wrong chart can obscure insights or mislead your audience. Here's a quick guide based on common data relationships:

• Comparing Values (How much/many?):

  • Bar Chart: Comparing discrete categories (e.g., sales by region, number of customers by product).
  • Column Chart: Similar to bar, but typically for categories or time series if few data points.
  • Ranked Bar/Column Chart: For showing top N or bottom N items.
  • Bullet Chart: For comparing actual performance against a target.

• Showing Composition (Parts of a whole?):

  • Pie Chart/Donut Chart: (Use sparingly for 2-5 categories, where the sum is 100%). Overuse makes comparisons difficult.
  • Stacked Bar/Column Chart: For showing how composition changes across categories or over time.
  • Treemap: For hierarchical data, showing proportion by area.

• Illustrating Distribution (How is data spread?):

  • Histogram: Showing the frequency distribution of a continuous variable.
  • Box Plot: Showing the spread, median, quartiles, and outliers of a numerical dataset, good for comparing distributions across groups.
  • Density Plot (Kernel Density Estimate): Showing the probability density function of a continuous variable.

• Analyzing Trends Over Time (How has it changed?):

  • Line Chart: Showing trends of one or more continuous variables over time (most common for time series).
  • Area Chart: Similar to line chart, but useful for showing cumulative totals over time.

• Showing Relationships/Correlations (What's the connection?):

  • Scatter Plot: Showing the relationship between two continuous variables. Useful for identifying clusters, outliers, and correlations.
  • Bubble Chart: Similar to scatter, but adds a third dimension (size of bubble).
  • Heatmap: Showing relationships between two categorical variables, or the strength of a correlation matrix.

• Displaying Geographical Data (Where is it?):

  • Choropleth Map: Shading regions based on a data value (e.g., population density by county).
  • Symbol Map (Proportional Symbol Map): Placing symbols on a map where size or color indicates a value.

• Text/Qualitative Data:

  • Word Cloud: (Use with caution, often more decorative than informative).
  • Bar Chart of Frequencies: Categorizing text data and counting occurrences.

Phase 3: Design and Refine Your Visualization

1. Choose Your Tool:

   • Spreadsheets: Excel, Google Sheets (good for basic charts).
   • Business Intelligence (BI) Tools: Tableau, Power BI, Qlik Sense (powerful, interactive dashboards).
   • Programming Libraries: Python (Matplotlib, Seaborn, Plotly, Altair), R (ggplot2) (most flexible, for custom and complex visualizations).
   • Online Chart Makers: Canva, Piktochart, Datawrapper (user-friendly for quick, shareable charts).

2. Follow Design Best Practices:

   • Clarity over Clutter: Remove unnecessary elements (chart junk).
   • Appropriate Scaling: Start Y-axes at zero for bar/column charts to avoid misleading comparisons. For line charts, choose scales that highlight trends without distortion.
   • Clear Labels and Titles:
     • Chart Title: Concise and descriptive, conveying the main message.
     • Axis Labels: Clearly indicate what each axis represents, including units.
     • Data Labels: Add labels to bars/points if necessary, especially for precise values.
     • Legend: Only if multiple data series are present, placed logically.
   • Strategic Color Use:
     • Use color to highlight key data points or categories.
     • Be mindful of colorblindness (use color-blind friendly palettes).
     • Avoid using too many colors; it can be distracting.
     • Use consistent color schemes across related visualizations.
   • Font Choice: Legible fonts, consistent sizing.
   • Whitespace: Allow for breathing room around elements.
   • Consistency: Maintain consistent styling across multiple charts in a report/dashboard.

3. Iterate and Get Feedback:

   • Create a draft.
   • Ask someone from your target audience to review it.
   • Is the message clear? Is it easy to understand? Are there any ambiguities?
   • Refine based on feedback.

Example Walkthrough: Sales Data

Presented Information (Raw Data):

Phase 1: Understand Data & Goal

• Data Type: Monthly sales (temporal, quantitative). Products (categorical).
• Goal: Show product sales trends over time, and compare performance between products.
• Audience: Sales team, interested in performance and identifying top/bottom performers.

Phase 2: Choose Visualization Type

• Line Chart: Best for showing trends over time for multiple series. We can see how each product performs month-to-month and how they compare against each other.

Phase 3: Design and Refine

• Tool: Excel or any BI tool.
• Design:
  • Title: "Monthly Sales Performance by Product (Jan-May)"
  • X-axis: "Month" (Jan, Feb, Mar, Apr, May)
  • Y-axis: "Sales Units"
  • Lines: Separate lines for Product A, B, and C.
  • Colors: Distinct colors for each product.
  • Legend: Clear legend to identify each product line.

What the visualization would show: A line chart where the X-axis is months and the Y-axis is sales units. Three distinct lines, one for each product, show their individual sales trajectories. You would immediately see that Product A has the highest sales, and all products show a general upward trend over the five months.

By following these steps, you can transform raw data into powerful visual stories that communicate insights effectively.

Creating effective data visualizations from presented information involves a systematic process. It's not just about picking a chart type; it's about understanding your data, your audience, and your message.

Here's a step-by-step guide on how to create data visualizations, assuming "presented information" means you already have your raw data:

Phase 1: Understand Your Data and Your Goal

1. Understand Your Data:

   • What type of data do you have?
     • Quantitative (Numerical): Discrete (counts, e.g., number of students) or Continuous (measurements, e.g., temperature, sales revenue)