Data Analytics tools and techniques

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Data analytics tools and techniques help organizations extract valuable insights from data. Common tools include Excel, Tableau, Power BI, Python (with libraries like Pandas and Matplotlib), R, and SQL for data manipulation, visualization, and analysis. Techniques encompass descriptive analytics (summarizing data), diagnostic analytics (identifying causes), predictive analytics (forecasting trends), and prescriptive analytics (recommending actions). Machine learning algorithms, such as regression, classification, and clustering, are also widely used. Data cleaning, normalization, and transformation are crucial pre-analysis steps. These tools and techniques enable data-driven decision-making, optimize operations, and enhance business strategies across industries.

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