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Model Development and Validation: Building machine learningmodels tailored to business problems such as customer churn prediction, fraud detection, or demand forecasting. Validation techniques ensure models perform well on unseen data. Data Manipulation: Pandas, NumPy, dplyr.
Skills and Tools of Data Scientists To excel in the field of Data Science, professionals need a diverse skill set, including: Programming Languages: Python, R, SQL, etc. Machine Learning: Supervised and unsupervised learning techniques, deeplearning, etc. Big Data Technologies: Hadoop, Spark, etc.
NoSQL Databases NoSQL databases do not follow the traditional relational database structure, which makes them ideal for storing unstructured data. They allow flexible datamodels such as document, key-value, and wide-column formats, which are well-suited for large-scale data management.
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