2. INTRODUCTION
Data Visualization
Data visualization is a powerful tool that allows us to explore and analyze
complex data sets in a way that is intuitive and easy to understand.
By representing data visually, we can quickly identify patterns, trends, and
outliers that might be missed by simply looking at raw numbers or tables of data.
Exploratory Analysis
Exploratory analysis is the process of examining data to understand its
underlying patterns and relationships.
It involves summarizing and visualizing data to identify trends, outliers, and other
insights that can help inform further analysis.
For example, if an analyst notices a spike in sales during a particular month,
they may investigate further to determine what factors contributed to the
increase.
In this presentation, we will discuss different types of data visualization
techniques available for exploratory analysis, why data visualization is so important
and how it can help us make sense of complex data sets
3. Data Visualization Techniques
Types of data visualization
There are many different types of techniques available to help you gain insights
from your data.
Some of the most common types of data visualizations include bar charts, line
charts, scatter plots, heat maps, histograms, box plots and pie charts.
Each type of visualization has its own strengths and weaknesses, and choosing the
right one for your data can make all the difference in your analysis.
Data Visualization Principles
There are some principles for effective data visualization:
• Clarity and simplicity
• Accuracy and honesty
• Consistency and context
• Appropriate chart selection
For example, scatter plots are useful for identifying patterns and relationships
between variables. Heat maps are ideal for visualizing large datasets.
Popular data visualization tools and libraries are Tableau ,ggplot2 (R) ,Matplotlib
(Python) ,D3.js (JavaScript) ,Power BI and most commonly used excel.
4. Exploratory Data Analysis Process
Steps in exploratory data analysis:
• Data cleaning and preprocessing.
• Univariate analysis (e.g., histograms, box plots).
• Bivariate analysis (e.g., scatter plots, correlation matrices).
• Multivariate analysis (e.g., heatmaps, parallel coordinates).
• Iterative data exploration and visualization.
Designing Effective Visuals
• Choosing the right chart for the data type.
• Selecting appropriate color palettes and labels.
For example, if you're trying to compare values across categories, a bar
chart may be more effective than a pie chart. Similarly, if you're looking for
trends over time, a line chart may be more appropriate than a scatter plot.
5. Case Study
The Dataset
The given dataset includes the network through which
Ads are airing, types of network like Cable/ Broadcast
and the show name also on which Ads got aired.
Insight
Based on the data from 2021, suggest a media plan to the
CMO of xyz. Which audience should they target?
On day 6 and day 7 that is Saturday and Sunday, xyz
should increase its ads placement in Overnight daypart
as it is a weekend and working population have time
and love to watch cable till late nights on weekend thus
xyz can have higher viewership of their ads at a lower
spend.
6. Future Trends in Data Visualization
Data Democratization
Real-time Visualization
Mobile and Social Data Visualization
Video Visualization
Artificial Intelligence and Machine Learning Data
One of the trends we can expect to see in the future of data
visualization is real-time data visualization.
Real-time data visualization allows users to see data as it is being
generated, providing instant insights and enabling quick decision-
making.
7. Conclusion
In conclusion, we have explored the importance of data visualization
techniques in exploratory analysis.
By using visual representations of data, we are able to identify patterns, that
may not be apparent in raw data. This allows us to gain insights and make
informed decisions based on our findings.
Each technique has its own strengths and weaknesses and it is important to
choose the right one for the given dataset and research question.
In today's data-driven world, the ability to analyze and interpret data is
crucial.
Data visualization techniques provide a powerful tool for exploratory
analysis and can help us uncover hidden insights that would otherwise go
unnoticed
References
1. Principles-of-Data-Visualization-for-Exploratory-Data-Analysis
2. MAO –Thesis-2015
THANK YOU!
