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DATA SCIENCE VS DATA ANALYTICS CRITERIA DATA SCIENCE DATA ANALYTICS Data Science involves a broader Data Analytics has a narrower focus, scope, encompassing data collection, concentrating on data analysis for the SCOPE cleaning, exploration, advanced purpose of generating actionable statistical analysis, machine learning, insights to support immediate and the development of data-driven business decisions. solutions. Data Science aims to gain a deep Data Analytics is goal-oriented, understanding of data, discover focusing on answering specific GOALS patterns, and create predictive questions or addressing immediate models to solve complex, long-term business needs with the aim of problems. optimizing current operations. Data Scientists typically require Data Analysts primarily need skills in advanced skills in programming (e.g., data cleaning, data visualization, and SKILL SET Python, R), machine learning, proficiency in data analysis tools like statistical analysis, data engineering, Excel, SQL, and business intelligence domain expertise, and data software. visualization. Data Science uses specialized tools and libraries like Jupyter, scikit-learn, Data Analytics employs tools such as TOOLS AND TensorFlow, and PyTorch for machine Microsoft Excel, SQL, Tableau, Power learning and data analysis. It often BI, and other data visualization TECHNOLOGY involves big data technologies like software. Hadoop and Spark. DATA Data Science frequently deals with Data Analytics often deals with large and unstructured datasets, structured and smaller datasets that VOLUME requiring expertise in handling can be managed using big data. traditional tools. Data Science focuses on solving PROBLEM Data Analytics concentrates on solving complex, open-ended problems by well-defined, specific business SOLVING developing data-driven models and questions or challenges. solutions.