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Applications of Data Science | IABAC

Data science uses data analysis, machine learning, and statistical techniques to solve real-world problems. Its applications include predicting trends, optimizing business processes, personalizing marketing, detecting fraud, and supporting decision-making across industries like healthcare, finance, and technology.

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Applications of Data Science | IABAC

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  1. Applications of Data Science www.iabac.org

  2. Agenda • • • • • Introduction to Data Science Healthcare Finance Retail Transportation www.iabac.org

  3. Introduction to Data Science What is Data Science? Key Components Importance Today Data Science is an interdisciplinary field focused on extracting knowledge and insights from data using scientific methods, processes, algorithms, and systems. It involves techniques from statistics, computer science, and domain expertise to analyze and interpret complex data. Data Science drives innovation, supports decision-making, and powers technologies like machine learning and artificial intelligence in various industries. www.iabac.org

  4. Healthcare Transforming Healthcare with Data Science ● Predictive analytics help forecast patient outcomes, enabling early intervention and better resource allocation. ● Personalized medicine tailors treatments based on individual patient data, improving efficacy and reducing side effects. ● Data Science enables the analysis of large-scale health data for epidemiological studies, identifying disease patterns and informing public health policies. ● Machine learning algorithms assist in medical imaging, improving diagnostic accuracy and speed. ● Wearable health devices generate real-time data, allowing continuous monitoring and proactive healthcare management. www.iabac.org

  5. Finance 1 2 3 Data Science algorithms detect anomalies in transaction patterns to identify potential Algorithmic trading uses predictive models to execute trades at optimal times, Risk management leverages data analytics to assess and mitigate financial risks, fraud in real-time. maximizing profitability. improving decision-making. www.iabac.org

  6. Retail Utilizing data to categorize customers based on purchasing behavior, preferences, and demographics to tailor marketing strategies effectively. Customer Segmentation Leveraging predictive analytics to optimize stock levels, reduce overstock and stockouts, and ensure timely replenishment of products. Inventory Management Applying machine learning models to predict future sales trends, helping businesses make informed decisions on production and sales strategies. Sales Forecasting www.iabac.org

  7. Transportation Data Science optimizes routes using real-time traffic data, reducing travel time and fuel consumption. Predictive maintenance uses data to foresee vehicle issues, minimizing downtime and repair costs. Autonomous driving systems rely on vast datasets to improve safety and efficiency of self-driving cars. Public transportation benefits from data-driven scheduling, enhancing service reliability and passenger satisfaction. www.iabac.org

  8. Thank you. www.iabac.org

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