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Basics of Data Visualization Analysis

via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

7 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Part of

Data Visualization with Excel and Python

Overview

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This course gives you a complete, practical toolkit for visualizing and analyzing data across more than 20 chart types. You'll identify which graph suits any dataset, configure it correctly, and present findings clearly to both specialist and non-specialist audiences. This course covers the full visualization landscape: core data types and chart elements, distributional techniques including histograms, density plots, strip plots, box plots, and violin plots, categorical methods including bar graphs, pie charts, and radar plots, relationship plots including scatter plots, lines of best fit, and line plots, and multi-dimensional graphics including bubble plots, matrix scatter plots, and contour plots. Whether you're new to data visualization or looking to sharpen your analytical eye, you'll finish with a structured framework for choosing the right chart every time. No prior visualization experience is required. If you've ever looked at a dataset and wondered how to make it communicate, this course gives you the tools to do exactly that.

Syllabus

Selecting the Right Visualization for Your Data

Every visualization decision starts before you open a graphing tool: it starts with a clear-eyed look at your data. In this module, you'll build the foundational skills to classify data types, interpret core graph elements, and select the right level of analysis so you can match any dataset to the visualization it deserves.

Visualizing Continuous Data Distributions

Continuous data can tell you exactly how a value is spread, but only if you choose the technique built for that job. In this module, you'll build practical judgment for selecting among histograms, density plots, strip plots, and box plots to match the distributional question you are actually trying to answer.

Visualizing Discrete Data Distributions

Discrete data comes with categories, and categories come with a question that looks obvious but rarely is: which technique actually fits the number of categories, the story you are telling, and the audience in front of you. In this module, you'll build judgment for selecting among bar graphs, dot plots, pie charts, and radar plots so you can match each technique to the discrete data challenge it was designed to solve.

Comparing Multiple Data Distributions

Visualizing one distribution is a solved problem. Visualizing six, ten, or eighteen at once introduces challenges of opacity, color, layout, and scale that can turn a useful chart into an unreadable one in seconds. In this module, you'll build practical judgment for configuring histograms, density plots, box plots, violin plots, bar graphs, and circular charts to compare multiple distributions without sacrificing the analytical clarity that makes any visualization worth building.

Revealing Relationships Between Variables

Relationships in data are rarely announced by the numbers themselves: they have to be rendered visible through the right visualization choice. In this module, you'll build the techniques to reveal continuous and discrete relationships, from raw scatter plots and fitted trend lines through time-ordered line plots to the table and mosaic formats that expose patterns between two categorical variables.

Extending Relationship Analysis to Multiple Dimensions

Standard distributions and bivariate relationships capture much of what data has to say — but some analytical questions require a third, fourth, or even fifth dimension to answer fully. In this module, you'll examine three techniques purpose-built for multi-dimensional visualization: matrix scatter and trellis plots for mapping many bivariate relationships simultaneously across a set of variables or groups, bubble plots for encoding a third continuous variable and a categorical fourth directly into the scatter structure, and contour plots for revealing how a z variable changes continuously across a two-dimensional x-y surface.

Conclusion

This course has covered a large range of visualization techniques — from histograms and bar charts through scatter plots and lines of best fit to contour plots and multi-panel matrices. The conclusion brings those techniques together around the one question every visualization project starts with: given this data and this analytical goal, where do I begin? You'll also look beyond the course's technical content to the craft dimensions that separate functional charts from compelling ones, including the perceptual and psychological principles that determine what a viewer actually sees when they encounter a visualization.