Data Visualization

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Use Power BI tools to identify and communicate business insights; deliver clear, static data visualizations and user-friendly reports; dashboards in Power BI. Find Data Visualization WFH freelancers on January 21, 2025 who work remotely. Read less

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From college-level to SAT, ACT, AP, find statisticians to teach

Math / Statistics

MATLAB

Hire developers in descriptive statistics for simple hypothesis tests with

Statistical Analysis

Mathcad

Build simulation models to solve real-world problem using Discrete Event

Simulation Modeling

Regression test planning; writing of test cases and test scripts;

Regression Testing

Interpret data from disparate sources, analyze results using stats, AI

Machine Learning

Design, build and delivery of analytics and reporting products and

Business Intelligence / Analysis

Graduates

Verified

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Wikipedia Data Visualization Courses

Top Frequently Asked Questions

What to Know

What are Data Visualization's 8 core principles?

Data visualization is an essential part of data analysis, helping to communicate complex information clearly and effectively. Here are the 8 core principles of data visualization:

1. Clarity
Simplicity: Aim for simplicity in design to make the data easy to understand. Avoid unnecessary clutter or decorations that don't add to the comprehension of the data.

2. Accuracy
Data Integrity: Ensure that the visualization accurately represents the data without distortion or misleading scales. Misleading visuals can lead to incorrect interpretations.

3. Relevance
Purpose-Driven: Every element in your visualization should serve a purpose towards answering a specific question or highlighting a particular insight. Irrelevant data or design elements should be omitted.

4. Efficiency
Maximize Data-Ink Ratio: As renowned American statistician Edward Tufte suggests, maximize the proportion of ink (or pixels) used to present data compared to the ink used for everything else. This principle encourages focusing on the data rather than the embellishments.

5. Consistency
Uniformity: Use consistent scales, colors, and styles across similar data sets or within a series of visuals. This helps in comparing data points or trends easily.

6. Context
Provide Context: Data visualization should not exist in a vacuum. It's important to include context through annotations, labels, or accompanying text to explain what the data means or why it's significant.

7. Design for Audience
Audience Awareness: Consider who will be viewing the data. The design should align with their level of expertise, needs, and how they will use the information. For instance, executives might prefer high-level summaries while analysts might need detailed, interactive data explorations.

8. Interactivity (where applicable)
Engagement: In digital formats, interactivity can enhance understanding by allowing users to drill down into specifics, filter data, or see different views of the data. However, this should be used judiciously to avoid overwhelming the user.

Additional Considerations:
Color Use: Colors should enhance comprehension, not confuse. Use color to distinguish categories or highlight important trends, but be mindful of color-blindness accessibility.
Proportionality: Ensure that the visual representation of quantities or proportions is true to the actual data values. Misrepresenting proportions can lead to misinterpretations.
Narrative: Sometimes, good data visualization tells a story. Organizing data in a way that leads the viewer through a narrative can be very powerful.
Ethics: Always represent data truthfully. Manipulating data or visuals to mislead is unethical.

By adhering to these principles, data visualization can become a powerful tool for communication, allowing complex data to be understood at a glance or explored in depth, depending on the user's need. These principles guide the creation of visualizations that are not only aesthetically pleasing but also functional and insightful.

How do math freelancers use data visualization?

Data visualization in mathematics serves as a bridge between abstract numerical or algebraic concepts and more tangible, visual interpretations. Here's how it works within the context of mathematics:

1. Representation of Mathematical Concepts
Functions and Equations: Graphs are used to visualize functions, showing how one variable changes with respect to another. For example, plotting y = x^2 gives a visual representation of a parabola, making it easier to understand its shape, symmetry, and behavior.
Geometric Shapes: Geometry benefits immensely from visualization. Complex shapes, transformations, or theorems like the Pythagorean theorem can be demonstrated visually, aiding in comprehension.
Complex Numbers: The complex plane allows for visualizing complex numbers, where the real part is plotted on the x-axis and the imaginary part on the y-axis, making concepts like magnitude, phase, and operations like multiplication more intuitive.

2. Pattern Recognition and Analysis
Sequences and Series: Visualizing sequences or series on a graph can help identify patterns, convergence or divergence, and other properties like periodicity or chaos.
Fractals: The visualization of fractals involves iterative mathematical processes, revealing self-similar patterns at different scales that are not easily discernible through equations alone.

3. Statistical Data Analysis
Descriptive Statistics: Histograms, box plots, and scatter plots visualize data distribution, central tendency, dispersion, and relationships between variables, making statistical concepts like mean, median, variance, or correlation more accessible.
Probability Distributions: Graphical representations of probability distributions (like normal, binomial, or Poisson) help in understanding the likelihood of different outcomes visually.

4. Solving and Understanding Problems
Optimization: Visualizing the objective function and constraints in optimization problems (like linear programming) makes it easier to grasp the concept of feasible regions and optimal solutions.
Differential Equations: Phase portraits, slope fields, or time-series plots can illustrate the behavior of solutions to differential equations, showing stability, limit cycles, or chaotic behavior.

5. Educational Tool
Pedagogy: For teaching, visualizations can simplify complex topics. For example, the concept of limits, derivatives, or integrals can be better understood through dynamic graphs or animations showing how these concepts affect functions.
Interactive Learning: Tools like GeoGebra allow for interactive exploration of mathematical concepts, where students can manipulate variables and immediately see the effects visually.

6. Data Exploration
Exploratory Data Analysis (EDA): Before formal analysis, plotting data can reveal outliers, trends, clusters, or anomalies, guiding mathematical modeling or further statistical tests.

7. Proofs and Theorems
Visual Proofs: Some proofs can be demonstrated visually, particularly in geometry or number theory, providing an intuitive understanding alongside or instead of formal proof methods.

8. Simulation and Modeling
Dynamic Systems: Visualizing how systems evolve over time through simulations can elucidate the mathematical models behind phenomena like population dynamics or economic models.

Techniques Used:
Graphs: Line graphs, bar graphs, pie charts, etc., for basic data representation.
Heatmaps: For showing matrix data or functions of two variables.
3D Plots: For functions with more than two variables or for surfaces in geometry.
Animations: To show how variables or functions change over time or with parameter adjustments.

Data visualization in mathematics not only aids in understanding and teaching but also in research, where new patterns or insights might be discovered through visual exploration. It transforms abstract math into something more concrete, accessible, and often more beautiful, fostering a deeper appreciation and comprehension of mathematical principles.

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