The document discusses data visualization in data science, highlighting its importance in representing data through tools like charts and graphs to uncover trends and patterns. It outlines various types of visualizations, such as line charts, bar charts, and scatter plots, while providing best practices and common pitfalls to avoid when creating visual representations. Additionally, it mentions popular data visualization tools like Zoho Analytics, Power BI, and Tableau.

1. DATA VISUALISATION IN
DATA SCIENCE
Tingom Ferdinand & Ngong Charlotte
Info@ferdsilinks.com

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

4. What to Consider
before Choosing a
Type of Visual

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​

6. Line Chart

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

8. Bar chart code in python

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

11. Some popular ones
include
• Zoho Analytics
• Power BI
• Tableau
• Qlik Sense
• Klipfolio

12. Ferdsilinks Group
www.ferdsilinks.com
02, Adjacent Presbyterian Church Molyko-Buea
237 Buea, Cameroon
Whatsapp: +237 652002630