DATA VISUALIZATION
• Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
• There are many types of visualizations such as bar charts, line charts, scatter plots and heat maps. For example:
o Bar charts can be used to compare categorical data.
o Line charts can be used to show changes over time.
o Scatter plots can be used to show relationships between two numerical variables.
o Heat maps can be used to show how a single variable changes across two dimensions.
• The choice of visualization depends on the type of data and the message you want to convey. For example:
o If you want to show how sales have changed over time for different products, you could use a line chart with time on the x-axis and sales on the y-axis. Each product would have its own line on the chart.
o If you want to compare the population of different countries, you could use a bar chart with countries on the x-axis and population on the y-axis.
• Good visualizations should be clear, accurate and engaging. Some tips for creating effective visualizations include:
o Choose an appropriate type of visualization for your data.
o Use color effectively to highlight important information.
o Label axes and provide a legend if necessary.
o Keep it simple – don’t clutter your visualization with too much information.
Some popular ones include:
1. Zoho Analytics
2. Power BI
3. Tableau
4. Qlik Sense
5. Klipfolio
These tools offer a range of features and capabilities to help you create effective visualizations
Common pitfalls to avoid when creating data visualizations:
Not flowing naturally: make sure your chart is easy to read and understand
Overcomplicating: avoid creating confusion by keeping it simple
Oversimplifying: don’t oversimplify your presentation at the expense of important information
Crowding: avoid crowding too much data into one chart
Lacking context: provide proper context for your data
Using trickery: don’t exaggerate or misrepresent your data
Inconsistent typography and colors: use consistent typography and colors for clarity
Lacking citations: provide authoritative citations for your data
2. Introduction
• Data visualization is the graphical
representation of information and data
• By using visual elements like charts,
graphs, and maps, data visualization tools
provide an accessible way to see and
understand trends, outliers, and patterns in
data
3. Types of visualizations
• Bar charts can be used to compare categorical
data
• Line charts can be used to show changes over
time
• Scatter plots can be used to show relationships
between two numerical variables
• Heat maps can be used to show how a single
variable changes across two dimensions
5. Line chart with python code
• If you want to show how sales have changed over time for different products, you could use a line chart
with time on the x-axis and sales on the y-axis
7. Bar Chart
If you want to compare
the population of different
countries, you could use a
bar chart with countries
on the x-axis and
population on the y-axis
9. Attributes of Good
visualizations or best
practices
• Choose an appropriate type of
visualization for your data
• Use color effectively to
highlight important information
• Label axes and provide a
legend if necessary
• Keep it simple – don’t clutter
your visualization with too
much information
10. Common
pitfalls to
avoid when
creating data
visualizations
Not flowing naturally: make
sure your chart is easy to
read and understand
Overcomplicating: avoid
creating confusion by
keeping it simple
Oversimplifying: don’t
oversimplify your
presentation at the
expense of important
information
Crowding: avoid crowding
too much data into one
chart
Lacking context: provide
proper context for your
data
Inconsistent typography
and colors: use consistent
typography and colors for
clarity
Lacking citations: provide
authoritative citations for
your data
