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Conduct a thorough assessment of the organization’s current data landscape.
Identify existing data sources, storage systems, and data quality issues.
Understand the business goals and challenges to align data strategy with organizational objectives.
Provide training programs to empower employees with the necessary skills for data-driven decision-making.
Implement change management strategies to facilitate the adoption of new data practices and technologies.

Collaborate with business stakeholders to identify tasks and processes suitable for data science integration.
Prioritize tasks based on potential impact, feasibility, and alignment with business goals.
Utilize domain knowledge to create new variables that enhance the predictive power of models.
Design solutions that can scale to handle growing data volumes and evolving business requirements.
Provide ongoing support and guidance to ensure the sustained success of data initiatives.

Gain a deep understanding of the existing data, including its structure, patterns, and potential issues.
Conduct statistical analysis to summarize and describe key features of the data.
Evaluate various machine learning algorithms based on the nature of the problem and characteristics of the data.
Implement techniques to make complex models more interpretable for stakeholders.
Provide detailed documentation on the EDA process, model development, and deployment.