Data Visualization

Intermediate 230 minutes 10 lessons

Turn numbers into insight — choose the right chart, design effective graphics, and master the scientific computing tools (MATLAB, R, Scilab, LabVIEW, Python) used to visualize and analyze data.

Learning Objectives

Select appropriate chart types for different data and audiences
Apply principles of visual perception and design to graphs
Construct histograms, box plots, scatter plots, and heat maps
Interpret and critique misleading visualizations
Combine descriptive statistics with effective graphical summaries
Use MATLAB, R, Scilab, LabVIEW, and Python for scientific visualization

Visualization Gallery — Chart Types at a Glance

Bar Chart

Bar Chart — Compares discrete categories side by side using rectangular bars whose lengths are proportional to the values they represent. Bars can run vertically (column chart) or horizontally.
Comparisons Rankings Survey Results

Line Chart

Line Chart — Shows trends over a continuous interval (usually time). Data points are connected by straight line segments, making it easy to spot upward/downward trends and rate of change.
Time Series Trends Forecasting

Scatter Plot

Scatter Plot — Plots individual data points on two axes to reveal relationships, clusters, or outliers. Adding a trend line (regression) shows correlation direction and strength.
Correlation Regression Outliers

Pie & Donut Chart

Pie & Donut Chart — Displays proportions of a whole. Each slice's arc length is proportional to its value. Donut variant adds a hollow center, often used for a summary stat.
Proportions Market Share Budgets

Histogram

Histogram — Groups continuous data into bins and shows the frequency (count) of observations in each bin. Unlike a bar chart, bins are contiguous with no gaps — revealing the shape of a distribution.
Distributions Frequency Normal Curves

Box Plot (Box & Whisker)

Box Plot — Summarises a distribution with five statistics: minimum, Q1, median (yellow line), Q3, and maximum. Whiskers extend to the farthest non-outlier points; outliers appear as individual dots.
Spread Outliers Comparison

Heat Map

Heat Map — Uses colour intensity to represent magnitude in a matrix. Darker or warmer colours indicate higher values. Excellent for spotting patterns across two categorical dimensions.
Correlation Matrices Activity Grids Geospatial

Area Chart

Area Chart — Similar to a line chart but with the region below the line filled in, emphasizing volume over time. Stacked area charts show how parts contribute to a total.
Cumulative Trends Part-to-Whole Revenue Streams

Bubble Chart

Bubble Chart — Extends a scatter plot with a third variable mapped to bubble size. Position encodes two numeric axes; area encodes the third variable, enabling three-dimensional comparison on a 2-D plane.
3-Variable Population Market Analysis

Radar (Spider) Chart

Radar (Spider) Chart — Plots multivariate data on axes radiating from a centre. Each axis represents a variable; the data polygon reveals strengths and weaknesses at a glance.
Multi-Attribute Performance Skill Profiles

Candlestick Chart

Candlestick Chart — Shows open, high, low, and close prices for each time period. Green (bullish) means the close was above the open; red (bearish) means it closed lower. Widely used in financial analysis.
Finance Stock Market OHLC Data

Waterfall Chart

Waterfall Chart — Visualises how an initial value is affected by a sequence of positive and negative changes, leading to a final total. Green bars add; red bars subtract; a summary bar shows the result.
Profit & Loss Budget Variance Incremental Changes