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Python’s flexibility extends to its ability to handle a wide range of tasks, from quick scripting to complex datamodelling. This versatility makes Python perfect for developers who want to script applications, websites, or perform data-intensive tasks. It is essential for performing advanced statistical computations.
Their primary responsibilities include: Data Collection and Preparation Data Scientists start by gathering relevant data from various sources, including databases, APIs, and online platforms. They clean and preprocess the data to remove inconsistencies and ensure its quality. ETL Tools: Apache NiFi, Talend, etc.
The process of statistical modelling involves the following steps: Problem Definition: Here, you clearly define the research question first that you want to address using statistical modeling. Data Collection: Based on the question or problem identified, you need to collect data that represents the problem that you are studying.
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