DS-Visualization-Unit-4 COMPUTER SCIENCE.pdf
- 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. • 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. • 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. • 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 • The rise of real-time data collection and analysis. • The increasing use of artificial intelligence (AI) and machine learning (ML) for data analysis. • The growing popularity of cloud-based data collection and analysis tools. • The increasing focus on data privacy and security
- 7. Some of the most popular technologies for data visualization in data science: • 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. • 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: Plotly is 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. Qlik Sense: Qlik Sense 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: • Shiny is an R package that facilitates the creation of interactive web applications directly from R scripts. • It allows data scientists to build dynamic dashboards, visualizations, and data- driven web interfaces without extensive web development knowledge. • Shiny apps can be hosted online or deployed on local servers.
- 10. R Markdown: • R Markdown is a versatile tool for creating reproducible reports, documents, and presentations that integrate R code, visualizations, and narrative text. • It enables data scientists to weave code, output, and text into a single document, making it easy to share insights and analysis.
- 11. Plumber: • Plumber is an R package for building APIs (Application Programming Interfaces) using R code. • Data scientists can create RESTful APIs to expose R models, functions, or data processing pipelines for integration with other applications. RStudio Connect: • RStudio Connect is a platform that allows you to publish and share Shiny apps, R Markdown documents, and Plumber APIs securely within your organization. • It simplifies the deployment and management of R-based applications.
- 12. R Packages: • R allows you to develop custom R packages that encapsulate functions, data, and documentation for specific data science tasks. • Packages can be shared and reused across projects, enhancing code modularity and reusability. R with SQL Databases: • R can be integrated with SQL databases using packages like RMySQL or DBI, enabling data retrieval, analysis, and visualization directly from databases. • This is useful for building data-driven applications that fetch and process data from relational databases.