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This comprehensive blog outlines vital aspects of DataAnalyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.
Summary : This article equips DataAnalysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for DataAnalysts to communicate effectively, collaborate effectively, and drive data-driven projects.
What is the difference between data analytics and data science? Data science involves the task of transforming data by using various technical analysis methods to extract meaningful insights using which a dataanalyst can apply to their business scenarios. Decisiontrees are more prone to overfitting.
Statistics Descriptive statistics includes techniques like mean, median, and standard deviation to help summarise data. Hypothesistesting and regression analysis are crucial for making predictions and understanding data relationships. They also optimise algorithms to ensure robust performance in real-world applications.
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