Data-Visualization-with-Python-2 PPT.pptx

Data-Visualization-with-Python-2 PPT.pptx

• 1.
  Unveiling Insights: Data Visualizationwith Python Welcome to a journey into the captivating realm of data visualization using Python. This presentation will guide you through the art of transforming raw data into compelling visual stories. by Dr. Sitanath Biswas
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  Interactive Visualizations withPlotly Interactive Exploration Plotly empowers users to delve deeper into data by allowing for zooming, panning, and hovering over specific data points. Customization It offers extensive customization options, enabling you to fine- tune the appearance and interactivity of your visualizations.
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  Geospatial Data Visualization 1 MappingLibraries Python libraries like Geopandas and Folium facilitate the creation of interactive maps. 2 Data Integration Integrating geographical data with other datasets enables spatial analysis and visualization. 3 Visual Insights Geospatial visualizations help identify trends, patterns, and relationships within geographical contexts.
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  Handling Large Datasets 1 DataChunking Large datasets can be processed in chunks to manage memory usage effectively. 2 Optimized Libraries Libraries like Dask and Pandas offer optimized functions for handling large datasets. 3 Parallel Processing Leveraging parallel processing can significantly accelerate data analysis tasks.
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  Machine Learning and Visualization 1Model Training Visualizations help monitor model training progress and identify potential issues. 2 Feature Engineering Data visualizations guide feature selection and transformation for optimal model performance. 3 Model Evaluation Visualizing model predictions and performance metrics facilitates model evaluation and comparison.
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  Case Study: AnalyzingReal- world Dataset Data Acquisition The process of collecting and preparing the real-world dataset is crucial. Data Exploration Visualizations help identify patterns, trends, and outliers in the dataset. Insight Generation Data visualizations lead to meaningful insights and actionable conclusions.
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  Best Practices inData Visualization Clarity Use clear labels, consistent color schemes, and appropriate chart types for effective communication. Conciseness Avoid information overload by focusing on key insights and minimizing unnecessary details. Context Provide context and background information to help the audience understand the data and its implications.
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  Tools for DashboardCreation Dash A powerful framework for building interactive web dashboards. Plotly Dash A popular library for creating customizable and interactive dashboards. Streamlit A lightweight framework for building web applications, including dashboards.
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  Sharing and PresentingData Insights 1 Visual Storytelling Transform data insights into engaging narratives for effective communication. 2 Interactive Reports Share interactive reports that allow users to explore data on their own. 3 Data Collaboration Enable data sharing and collaboration among team members.
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  Future Trends inData Visualization