After understanding why we visualize data, the next step is learning how. Several standard chart types serve as the building blocks for visual data exploration and communication. Each type is suited for different kinds of data and different messages. Let's look at some of the most frequently used ones.

Bar Charts

Bar charts are excellent for comparing numerical values across different categories. They use rectangular bars, either vertical or horizontal, where the length of the bar is proportional to the value it represents.

Use Case: Comparing quantities for distinct groups or categories. Examples include monthly sales figures, website traffic by source, or feature usage counts.
Variations: Can be vertical (column chart) or horizontal. Grouped bar charts compare sub-categories within each main category, while stacked bar charts show how a total is divided into parts across categories.

Here is a simple example comparing the number of fruits sold in a week:

Quantity of different fruit types sold during the week.

Bar charts make it easy to see at a glance which category has the highest or lowest value and to compare magnitudes between categories.

Line Charts

Line charts are most effective for showing trends over time or another continuous sequence. Data points are plotted and connected by lines, making it easy to visualize increases, decreases, or fluctuations.

Use Case: Tracking changes over a continuous interval, like time (days, months, years). Examples include stock price movements, temperature changes throughout a day, or website visitor trends over several months.
Variations: Multiple lines can be plotted on the same chart to compare trends for different groups. Area charts are similar but shade the area under the line, which can emphasize volume or cumulative totals.

Consider this visualization of website visitors over a week:

Number of website visitors recorded each day over one week.

Line charts clearly illustrate the direction and rate of change.

Pie Charts

Pie charts represent proportions or percentages, showing how a whole is divided into parts. The entire circle (the "pie") represents 100%, and the size of each "slice" corresponds to its portion of the whole.

Use Case: Displaying the composition of a whole, where each category represents a percentage. Examples include market share distribution, budget allocation by department, or survey responses broken down by choice.
Caution: Pie charts become difficult to read and compare accurately if there are too many slices or if the slices represent very similar proportions. They are generally best for showing a few distinct parts of a whole. Bar charts are often a better alternative for precise comparisons between categories.

This pie chart shows the distribution of responses to a simple survey question:

Percentage distribution of favorite seasons based on survey responses.

Use pie charts sparingly and when emphasizing simple part-to-whole relationships.

Scatter Plots

Scatter plots are used to visualize the relationship between two numerical variables. Each point on the plot represents an observation (like a single customer or data entry), positioned according to its values on the two axes.

Use Case: Identifying correlations or patterns between two continuous variables. Examples include plotting height vs. weight, advertising spend vs. sales revenue, or study hours vs. exam scores.
Interpretation: The pattern of the plotted points can indicate a positive relationship (points trend upwards), a negative relationship (points trend downwards), or no clear relationship. They can also help identify outliers, which are points far removed from the general pattern.

Let's visualize the relationship between hours spent studying and exam scores:

Relationship between the number of hours students studied and their corresponding exam scores.

Scatter plots are fundamental tools in exploratory data analysis for understanding potential connections between variables before applying more complex modeling techniques.

These four chart types form a basic toolkit for data visualization. Understanding their strengths and appropriate use cases is a fundamental skill in data science. As you progress, you'll encounter many other specialized chart types, but these provide a solid foundation for visually exploring and presenting data.