#63 The Past and Present of Data Science
DataFramed
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Navigating Data Science Challenges
This chapter explores the evolution of data science from experimentation to operationalization, addressing the impact of centralized data tools on efficiency. It discusses the divide between legacy companies and modern data-driven organizations, emphasizing the need for leadership buy-in and effective communication strategies. The importance of aligning data science projects with organizational goals and understanding data cleanliness is also highlighted to ensure successful outcomes.
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