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Data Collection and Cleaning: Collecting, cleaning, and preprocessing data from various sources to ensure accuracy and reliability for analysis.
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Exploratory Data Analysis (EDA): Performing EDA to understand data patterns, identify trends, and formulate hypotheses.
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Model Development: Developing predictive models using advanced statistical and machine learning techniques to solve complex business problems.
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Model Evaluation and Optimization: Evaluating model performance, fine-tuning parameters, and optimizing algorithms for better accuracy and efficiency.
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Insights Generation: Communicating insights and findings to stakeholders through reports, visualizations, and presentations.
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Collaboration: Collaborating with cross-functional teams including data engineers, software developers, and business analysts