Visualization topic of big data analytics

What is visualization?
• Visualize: “To form a mental vision, image, or picture of (something not visible or present to
the sight, or of an abstraction); to make visible to the mind or imagination.”
• Visualization is the use of computer graphics to create visual images which aid in the
understanding of complex, often massive representations of data.
• Visual Data Mining is the process of discovering implicit but useful knowledge from large
data sets using visualization techniques.
• 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.

What is visualization?
• Additionally, it provides an excellent way for employees or business owners
to present data to non-technical audiences without confusion.
• In the world of Big Data, data visualization tools and technologies are
essential to analyze massive amounts of information and make data-driven
decisions.
• Data visualization is used in various fields, including data analysis, business
intelligence, scientific research, and decision-making.

IMPORTANCE OF VISUALIZATION
• Data visualization is the graphical representation of data to help people
understand and interpret complex information, patterns, and
relationships within datasets.
• Whether simple or complex, the right visualization can bring everyone on
the same page, regardless of their level of expertise.
• It is a fundamental aspect of data analysis and communication because it
can make data more accessible, understandable, and actionable.

DATA VISUALIZATION AND BIG DATA
• As the “age of Big Data” kicks into high gear, visualization is an increasingly key tool
to make sense of the trillions of rows of data generated every day.
• Data visualization helps to tell stories by curating data into a form easier to
understand, highlighting the trends and outliers.
• A good visualization tells a story, removing the noise from data and highlighting
useful information.
• However, it’s not simply as easy as just dressing up a graph to make it look better
or slapping on the “info” part of an infographic.

DATA VISUALIZATION AND BIG DATA
• Effective data visualization is a delicate balancing act between form and
function.
• The plainest graph could be too boring to catch any notice or it make tell a
powerful point; the most stunning visualization could utterly fail at conveying
the right message or it could speak volumes.
• The data and the visuals need to work together, and there’s an art to
combining great analysis with great storytelling.

What are the main types of data visualization?
• The main types of data visualization include
• charts,
• graphs and
• maps

What are the main types of data visualization?
• In the form of
• line charts,
• bar graphs,
• tree charts,
• dual-axis charts,
• mind maps,
• funnel charts and
• heatmaps.

ADVANTAGES
• Our eyes are drawn to colors and patterns. We can quickly identify red from blue, and squares
from circles. Our culture is visual, including everything from art and advertisements to TV and
movies.
• Data visualization is another form of visual art that grabs our interest and keeps our eyes on the
message. When we see a chart, we quickly see trends and outliers. If we can see something, we
internalize it quickly.
• It’s storytelling with a purpose. If you’ve ever stared at a massive spreadsheet of data and
couldn’t see a trend, you know how much more effective a visualization can be.
• Easily sharing information.
• Interactively explore opportunities.
• Visualize patterns and relationships.

DISADVANTAGES
• When viewing a visualization with many different data points, it’s easy to
make an inaccurate assumption.
• Sometimes the visualization is just designed wrong so that it’s biased or
confusing.
• Correlation doesn’t always mean causation.
• Core messages can get lost in translation.

TRADITIONAL VISUALIZATION
• Traditional visualization refers to the historical methods and techniques used to
create visual representations of data before the advent of modern computer-
based tools and technologies.
• Traditional data visualization methods include charts, graphs, diagrams, and
other manual or analog techniques.
• These methods have been used for many years and are often considered
fundamental in various fields, such as science, engineering, business, and
education.

TRADITIONAL VISUALIZATION METHODS
Some traditional visualization methods and examples include:
1. Bar Charts: Bar charts use rectangular bars of varying lengths to represent data
values. They are commonly used to compare and display data across different
categories. For example, the graph shows how many students like which season. The
seasons are listed as spring, summer, fall, or winter on the bottom horizontal axis of
the graph. The number of students is written on the vertical axis as 0, 1, 2. The
seasons on the x-axis represent the categorical data and the number of students on
the y-axis represents the numerical possible values. And the blue bars represent the
number of students pertaining to each category or season.

1. Bar Charts:

Advantages of Bar charts:
• Bar graph summarises the large set of data in simple visual form.
• It displays each category of data in the frequency distribution.
• It clarifies the trend of data better than the table.
• It helps in estimating the key values at a glance.
Disadvantages of bar charts:
• Sometimes, the bar graph fails to reveal the patterns, cause, effects, etc.
• It can be easily manipulated to yield fake information.

2. Line Charts: Line charts display data
points as a series of connected points
on a graph, typically using lines. They
are effective for showing trends and
changes over time. For instance, a
line chart can illustrate the number
of books it sold each week during a
certain period.

2. Line Charts:

3. Pie Charts: Pie charts are circular graphs divided into slices, with each
slice representing a proportion of a whole. They are used to show the
distribution of a single data set. The sum of all the data is equal to 360°.
The total value of the pie is always 100%.
Therefore, the pie chart formula is given as
(Given Data/Total value of Data) × 360°

3. Pie Charts:

4. Scatter Plots: Scatter plots use individual data points on a two-
dimensional graph to display the relationship between two variables such
as correlation. They are useful for identifying patterns or correlations in
data. An example would be a scatter plot showing the relationship
between a person's age and their cholesterol levels.

4. Scatter Plots:

5. Histograms: Histograms are used to represent the distribution of data in a continuous or
discrete dataset.
They group data into "bins" and display the frequency of data points falling into each bin. For
instance, a histogram can illustrate the distribution of test scores in a class.
The histogram displays the distribution frequency as a two-dimensional figure, meaning the height
and width of columns or rectangles have particular meanings and can both vary.
A bar chart is a one-dimensional figure. The height of its bars represent something specific. The
width of the bars has no meaning.
On a histogram, there are no gaps between columns. Column width changes as the variable
represented changes. On bar charts, the bars usually have gaps between them.

Histograms:

6. Flowcharts: Flowcharts are used to
visualize processes or workflows. They
consist of shapes and arrows that
represent different steps or decision points
in a process. Flowcharts are often used in
business and engineering to depict
processes like manufacturing or decision-
making.

7. Maps: Maps are a form of geographical visualization that display spatial
data. They can be used to represent various information, such as
population density, weather patterns, or geographic features like
mountains and rivers. Example: Country to country migration.

Maps:

8. Infographics: Infographics combine various visualization techniques,
including charts, graphs, icons, and text, to convey complex information
in a visually appealing and understandable manner. They are often used
in journalism, marketing, and education to simplify and clarify data and
concepts.

Infographics:

TRADITIONAL VISUALIZATION
METHODS
Infographics:

Aspect Traditional Data Visualization Big Data Visualization
Scale of Data Small to medium-sized datasets Extremely large and complex datasets
Data Sources Structured data from well-defined sources
Unstructured and diverse data from various
sources
Processing Requirements Can often be performed on a single machine
Requires distributed computing and parallel
processing
Tools and Technologies
Spreadsheet software, charting libraries,
desktop applications
Distributed computing frameworks, in-memory
processing, specialized platforms
Interactivity Typically static or limited interactivity Offers rich interactive features for exploration
Challenges
Less likely to face challenges related to data
volume and complexity
Must address challenges related to massive
data, diverse sources, and real-time data
Scalability and Performance
May struggle with performance and
scalability for big data
Optimized for scalability and performance with
large datasets

Visualization topic of big data analytics

More Related Content

Similar to Visualization topic of big data analytics

Recently uploaded

Visualization topic of big data analytics