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Data analysis

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Summary

Data analysis

S00171
Version: 1.9

  • arable farming
  • horticulture
  • tree crops
  • viticulture
  • livestock farming
  • data analysis
  • data
  • Location: remote
  • Offered by: JR

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Description

Leveraging descriptive statistics is a fundamental approach to gain a comprehensive understanding of a dataset. It involves employing measures such as mean, median, and standard deviation to uncover central tendencies and variability. Beyond insight generation, statistical methods play a crucial role in enhancing the robustness of the model training process. Plausibility filtering involves assessing data for logical consistency, ensuring that values align with expected ranges or patterns. Simultaneously, outlier filtering identifies and mitigates aberrant data points that could potentially skew model predictions. This careful preparation of the dataset not only refines its quality but also contributes to the overall efficacy of the model training, fostering a more accurate and reliable learning process. Depending on the requirements, the aforementioned approaches can be performed in the service.