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Data Science Analytics | IABAC

To find patterns, insights, and trends, data science analytics entails gathering, cleaning, and analyzing data. In order to enable well-informed decision-making, optimize corporate procedures, forecast results, and stimulate innovation across industries, it integrates statistics, machine learning, and visualization.

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Data Science Analytics | IABAC

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  1. Data Science Analytics iabac.org

  2. Introduction What is Data Science Analytics? Combines statistics, computer science & business knowledge. Goal: Extract meaningful patterns and insights. iabac.org

  3. Importance Helps in decision-making Predicts future trends Improves efficiency & productivity Supports innovation iabac.org

  4. Key Components Data Collection Data Cleaning Data Analysis Data Visualization Decision Making iabac.org

  5. Tools & Technologies Python, R SQL Tableau, Power BI Machine Learning libraries iabac.org

  6. Data Science Process Define the problem Collect data Clean & prepare data Analyze data Build models Communicate results iabac.org

  7. Types of Analytics Descriptive (what happened) Diagnostic (why it happened) Predictive (what will happen) Prescriptive (what to do) iabac.org

  8. Applications Healthcare (disease prediction) Business (customer insights) Finance (fraud detection) E-commerce (recommendations) iabac.org

  9. Benefits Better decision-making Risk management Cost reduction Improved customer experience iabac.org

  10. Thank You Visit: iabac.org

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