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Key tools and techniques in the data science sector include programming languages like Python and R, which are essential for data manipulation, analysis, and visualization. Machine learning algorithms (ML Algorithms), such as decision trees, neural networks, and k-means clustering, are used to model and predict data patterns. Data wrangling and cleaning techniques are crucial for preparing raw data for analysis. Visualization tools like Tableau and Matplotlib help in creating insightful graphs and charts. Additionally, big data platforms like Hadoop and Spark are used for handling and processing large datasets, while SQL is fundamental for database management and querying. These tools and techniques collectively enable data scientists to extract meaningful insights and drive data-driven decisions.

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