DS-Visualization-Unit-4 COMPUTER SCIENCE.pdf

D DATA SCIENCE VISUALISATION UNIT-4 (COMPUTER SCIENCE)

1. BIG DATA
ANALYTICS
DR. OWAIS BHAT

3. DATA-SCIENCE APPLICATIONS
Data science is a rapidly growing field that is being used in a wide variety of
applications.
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Fraud detection:Data science can be used to identify and prevent fraudulent
transactions. For example, banks use data science to identify suspicious
activity in credit card transactions.

4. •
Customer segmentation:Data science can be used to segment customers into
groups based on their characteristics. This information can be used to target
customers with specific marketing messages.
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Product recommendations:Data science can be used to recommend products
to customers based on their past purchases or browsing history. This
information can be used to increase sales and improve customer satisfaction.
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Risk assessment:Data science can be used to assess the risk of an event
happening. For example, insurance companies use data science to assess the
risk of a customer filing a claim.

5. •
Personalized medicine:Data science can be used to personalize medical
treatment for patients. For example, doctors can use data science to identify
the best treatment for a patient based on their individual characteristics.

6. RECENT TRENDS IN DATA COLLECTION & ANALYSIS
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The rise of real-time data collection and analysis.
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The increasing use of artificial intelligence (AI) and machine learning (ML) for
data analysis.
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The growing popularity of cloud-based data collection and analysis tools.
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The increasing focus on data privacy and security

7. Some of the most popular technologies for data visualization in data science:
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Matplotlib:Matplotlib is a Python library for creating static, animated, and
interactive visualizations. It is a popular choice for data scientists because it is
easy to use and versatile.
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Seaborn:Seaborn is a Python library that builds on Matplotlib to provide a
high-level interface for creating attractive and informative visualizations. It is a
good choice for data scientists who want to create visualizations that are both
visually appealing and easy to understand.

8. Plotly:Plotlyis a Python library for creating interactive visualizations that can
be embedded in web pages or documents. It is a good choice for data scientists
who want to create visualizations that can be shared and explored online.
Tableau:Tableau is a commercial data visualization software that is known for
its ease of use and interactive capabilities. It is a good choice for businesses
and organizations that need to create data-driven visualizations for non-
technical audiences.
QlikSense:QlikSense is another commercial data visualization software that is
known for its speed and scalability. It is a good choice for businesses that need
to process large amounts of data quickly and create interactive visualizations.

9. DATA-SCIENCE –R LANGUAGE
R is a powerful programming language for data science, and it can be used to
develop a variety of applications. Some of the most common application
development methods in data science using R include:
Shiny:
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Shiny is an R package that facilitates the creation of interactive web
applications directly from R scripts.
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It allows data scientists to build dynamic dashboards, visualizations, and data-
driven web interfaces without extensive web development knowledge.
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Shiny apps can be hosted online or deployed on local servers.

10. R Markdown:
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R Markdown is a versatile tool for creating reproducible reports, documents,
and presentations that integrate R code, visualizations, and narrative text.
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It enables data scientists to weave code, output, and text into a single
document, making it easy to share insights and analysis.

11. Plumber:
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Plumber is an R package for building APIs (Application Programming
Interfaces) using R code.
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Data scientists can create RESTfulAPIs to expose R models, functions, or data
processing pipelines for integration with other applications.
RStudioConnect:
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RStudioConnect is a platform that allows you to publish and share Shiny apps,
R Markdown documents, and Plumber APIs securely within your organization.
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It simplifies the deployment and management of R-based applications.

12. R Packages:
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R allows you to develop custom R packages that encapsulate functions, data,
and documentation for specific data science tasks.
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Packages can be shared and reused across projects, enhancing code
modularity and reusability.
R with SQL Databases:
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R can be integrated with SQL databases using packages like RMySQLor DBI,
enabling data retrieval, analysis, and visualization directly from databases.
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This is useful for building data-driven applications that fetch and process data
from relational databases.

14. THANK YOU
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