This document introduces data visualization, detailing its importance, drivers, analytics applications, and various technologies. It outlines the lifecycle of data visualization analytics and highlights popular tools, both open-source and commercial, for effective visualization. The text also discusses the interplay between data visualization and cloud computing, predictive analytics, mobile business intelligence, and crowdsourcing in data analysis.

1. UNIT-1
INTRODUCTION TO Data Visualization
Topics:
 Data Visualization and its Importance,
 Drivers for Data Visualization,
 Introduction to Data Visualization Analytics,
 Data Visualization Analytics applications.
 Data Visualization TECHNOLOGIES:
 Hadoop’s Parallel World, Data discovery,
 Open-source technology for Data Visualization Analytics,
 cloud and Data Visualization,
 Predictive Analytics,
 Mobile Business Intelligence and Data Visualization,
 Crowd Sourcing Analytics,
 Inter- and Trans-Firewall Analytics, Information Management.

2. Data Visualization
• Data Visualization refers to extremely large
and complex datasets that cannot be easily
managed, processed or analysed using
traditional data processing tools.
Importance of Data Visualization:
Informed Decision Making
Improved Customer Experience
Innovation and Product Development
Operational Efficiency
Fraud Detection and Security

3. Smart Cities and Urban Planning
Scientific Research
Challenges and Opportunities
Technologies and Tools
Ethical Considerations
 Healthcare Advancements
 Optimizing Marketing Strategies

4. Drivers for
Data Visualization

5. 1. The digitization of society
2. The drop in technology costs
3. Connectivity through cloud computing
4. Increased knowledge about data science
5. Social media applications
6. The rise of Internet-of-Things (IoT).

6. Data
Visualization analytics
• Data Visualization analytics is the sometimes-
difficult process of analysing large amounts of
data in order to reveal information – such as
hidden patterns, correlations, market trends,
and consumer preferences – that may assist
businesses in making educated business
choices.
The Lifecycle Phases of Data Visualization Analytics:
Stage 1 - Business case evaluation
Stage 2 - Identification of data
Stage 3 - Data filtering

7. • Stage 4 - Data extraction
• Stage 5 - Data aggregation
• Stage 6 - Data analysis
• Stage 7 - Visualization of data
• Stage 8 - Final analysis result
Data Visualization Analytics Tools:
 Matplotlib:
• A widely-used 2D plotting library for Python.
• Provides a variety of chart types, enabling the creation of static, animated, and interactive visualizations.
• Versatile and a staple in the toolkit of many data scientists.
 Seaborn:
• Built on Matplotlib, Seaborn specializes in statistical data visualization.
• Simplifies the process of creating informative and attractive visualizations.
• Excellent for exploratory data analysis.

8.  Plotly:
• A versatile library supporting interactive visualizations and dashboards.
• Compatible with multiple programming languages, including Python, R, and Julia.
• Ideal for creating dynamic and interactive data visualizations.
 D3.js:
• A JavaScript library for producing dynamic, interactive data visualizations in web browsers.
• Provides full control over the visualization process.
• Powerful for creating custom and complex visualizations.
 Tableau Public:
• While not strictly open source, Tableau Public is noteworthy for its accessibility.
• Allows the creation and sharing of interactive charts, dashboards, and reports.
• A free version of Tableau’s data visualization platform with a user-friendly interface.

9. Exploring the top 10 open-source Data Visualization tools to stay ahead of
tech in the year 2023
1. D3.js:
1. Website: D3.js
2. Description: A powerful JavaScript library for creating interactive and dynamic data visualizations in the web
browser.
2. Matplotlib:
1. Website: Matplotlib
2. Description: A widely-used 2D plotting library for Python that produces high-quality static charts and graphs.
3. Plotly:
1. Website: Plotly
2. Description: A Python graphing library that makes interactive, publication-quality graphs online. It can be used
both online and offline.
4. Bokeh:
1. Website: Bokeh
2. Description: A Python interactive visualization library that targets modern web browsers for presentation.
5. Tableau Public:
1. Website: Tableau Public
2. Description: While Tableau itself is a commercial product, Tableau Public allows for free data visualization
sharing.

10. Exploring the top 10 open-source Data
Visualization tools to stay ahead of tech in the
year 2023
6. Grafana:
1. Website: Grafana
2. Description: An open-source analytics and monitoring platform. It supports various data sources and is commonly used for time-
series data.
7. Metabase:
1. Website: Metabase
2. Description: An open-source business intelligence tool that lets users ask questions about their data and create interactive
dashboards.
8. Chart.js:
1. Website: Chart.js
2. Description: A simple yet flexible JavaScript charting library for designers and developers to create interactive charts in web pages.
9. Vega-Lite:
1. Website: Vega-Lite
2. Description: A high-level grammar for creating visualizations using JSON syntax. It is built on top of D3.js and simplifies the
process of creating charts.
10. Redash:
1. Website: Redash
2. Description: An open-source data visualization and dashboarding platform that connects to various data sources to create
interactive visualizations.

11. cloud and Data
Visualization
• Data Visualization and cloud computing are two
distinctly different ideas, but the two
concepts have become so interwoven that they
are almost inseparable. It's important to
define the two ideas and see how they relate.
• The pros of Data Visualization in the cloud
• Scalability
• Agility
• Cost

12. • Accessibility
• Resilience
The cons of Data Visualization in the cloud
• Network dependence
• Storage costs
• Security
• Lack of standardization

13. Predictive
Analytics
 Predictive analytics uses statistics and
modelling techniques to determine future
performance.
 Industries and disciplines, such as insurance
and marketing, use predictive techniques to
make important decisions.
 Predictive models help make weather forecasts,
develop video games, translate voice-to-text
messages, customer service decisions, and
develop investment portfolios.

14.  People often confuse predictive analytics with
machine learning even though the two are
different disciplines.
 Types of predictive models include decision
trees, regression, and neural networks.
How Does Netflix Use Predictive Analytics?
• Data collection is very important to a company
like Netflix. It collects data from its
customers based on their behaviour and past
viewing patterns. It uses that information to
make recommendations based on their
preferences. This is the basis behind the
"Because you watched..." lists you'll find on
your subscription.

15. Mobile Business Intelligence and
Data Visualization
Mobile Business Intelligence (BI) refers to the
ability to access and perform BI-related data
analysis on mobile devices and tablets.
Why is
mobile BI important?
1.Businesses these days possess an abundance of data.
Everyone needs real-time data access to make data-
driven decisions anytime and anywhere in this fast-
paced environment.
2.The number of organizations using mobile apps
like SaaS for critical business processes is
increasing daily. Whether you are a CEO, salesperson,
digital marketer, department manager, or employee,
mobile BI can help you increase productivity, improve
the decision-making process, and boost your business.

16. Crowd Sourcing
Analytics
Crowd sourcing is the process of exploring
customers’ ideas, opinions and thoughts
available on the internet from large groups
of people aimed at incorporating innovation,
implementing new ideas, and eliminating
product issues.
Pros
 Crowdsourcing brings together communities around a
common project or cause
 Efficient way of solving time-intensive problems
 Deeper engagement by communities, who resonate and
build loyalty to the product or solution

17. Cons
 Results can be easily skewed based on the crowd being
sourced
 Lack of confidentiality or ownership of an idea
 Potential to miss the best ideas, talent, or direction
and fall short of the goal or purpose.

18. Real Life
Examples
Lay’s
Netflix Use Crowdsourcing
Waze

19. INFORMATION
MANAGEMENT
Information management refers to a
program or system inside an
organisation that controls the
procedures that regulate the
structure, processing, distribution,
and use of data. For the objectives
of business intelligence, information
management is essential.

20. Challenges of information
management
Collecting information
Making information available
Ensuring information is used

21. THANK YOU!!