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Engineering LibreTexts

9: Visualizing Data

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  • 9.0: Introduction
    This page discusses various data visualization techniques for univariate data, including boxplots, histograms, and Pareto charts. It highlights the importance of using Python and Matplotlib for creating these visualizations, providing examples like employee salaries and baseball player ages. The section also covers methods for analyzing distributions, such as identifying skewness and bimodal patterns.
  • 9.1: Encoding Univariate Data
    This page discusses data visualization methods focusing on univariate data, including boxplots, histograms, and Pareto charts, primarily using Python's Matplotlib library. It emphasizes the interpretation of boxplots for examining data distributions, outliers, and skewness, and provides practical examples such as a salary boxplot and analyses of baseball player ages and sales data.
  • 9.2: Encoding Data That Change Over Time
    This page outlines learning objectives for creating and interpreting time series graphs in Python, highlighting their significance across various fields. It emphasizes identifying trends and patterns in data for better decision-making, using an example of community college enrollment over eight years. The page discusses using Python's matplotlib library for visualization, with a focus on axes labeling and formatting for clarity.
  • 9.3: Graphing Probability Distributions
    This page covers learning objectives surrounding probability distributions, emphasizing the creation of graphs for visualization, interpretation of probabilities, and Python's role in data visualization. It introduces discrete and continuous distributions, notably the normal distribution, detailing their characteristics, applications, and statistical significance.
  • 9.4: Geospatial and Heatmap Data Visualization Using Python
    This page discusses learning objectives involving geospatial data visualization in Python, detailing spatial heatmaps, GIS mapping features, and the use of Pandas and Geopandas for data manipulation. It covers heatmap creation with Matplotlib and Seaborn, the collection and processing of geospatial data, and the significance of interactive mapping for data interpretation.
  • 9.5: Multivariate and Network Data Visualization Using Python
    This page discusses advanced data visualization techniques, including scatterplots, correlation heatmaps, and 3D graphs. It emphasizes the creation and interpretation of these visualizations using tools like Python's matplotlib and seaborn, particularly for large datasets. Additionally, it describes a code segment for generating a legend in a scatter plot based on unique species names and mentions the availability of datasets for download.
  • 9.6: Key Terms
    This page defines various data visualization and statistical concepts, including 3D visualization, bar graphs, and histograms. It explains terms such as binomial distribution, bivariate data, and boxplots, along with different graph types like choropleth graphs and correlation heatmaps.
  • 9.7: Group Project
    This page outlines a project where group members will research 15 vehicle models to collect data on their used car values, focusing on brand, model, type, age, and value. They will create a scatterplot to visualize the relationship between vehicle age and value, color-coded by vehicle type. The analysis aims to identify trends and assess how the scatterplot might assist in used vehicle purchases, emphasizing the importance of the color coding in decision-making.
  • 9.8: Critical Thinking
    This page discusses various statistical tasks focused on data visualization and interpretation. It includes creating boxplots, graphs for various distributions (binomial, Poisson, normal), and analyzing their symmetry and skewness. It also covers constructing a time series chart for social media subscriber trends over seven years and accessing Seaborn datasets to generate correlation heatmaps and 3D visualizations, followed by correlation analysis among variables.


This page titled 9: Visualizing Data is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by OpenStax via source content that was edited to the style and standards of the LibreTexts platform.

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