Data Science (DS) leverages Data Analytics to help organizations extract value from large datasets and enhance performance. A significant focus in DS is on data collection, cleaning, and transformation to create high-quality datasets for analysis. However, traditional manual data preparation methods are often inefficient and error-prone, particularly in Big Data environments. DataOps seeks to automate data lifecycle stages by integrating DevOps practices, enhancing the quality and reliability of
Monday, November 17, 2025
Belgrade, Serbia
Data Science (DS) leverages Data Analytics to help organizations extract value from large datasets and enhance performance. A significant focus in DS is on data collection, cleaning, and transformation to create high-quality datasets for analysis. However, traditional manual data preparation methods are often inefficient and error-prone, particularly in Big Data environments. DataOps seeks to automate data lifecycle stages by integrating DevOps practices, enhancing the quality and reliability of
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