Data Analytics Classes in Pune Data Quality Check: EDA assists in detecting data quality problems, like missing values, outliers, and inconsistencies.
All these issues should be resolved in the beginning to ensure the reliability and correctness of further analyses.
Hypothesis Development: EDA has the ability to trigger hypotheses and research questions.
Through graphical exploration of data, analysts might observe patterns or trends that give rise to hypotheses to be tested appropriately in subsequent analyses.
Feature Selection: EDA can help in the selection of meaningful features (variables) to be analyzed.
Knowing the most important or informative variables can simplify the modeling process and enhance model performance.
Data Preprocessing: EDA tends to indicate the necessity for data preprocessing activities, including data normalization, scaling, or transformation.
These activities can improve data quality and model performance.
Visualization: EDA entails the creation of different data visualizations, including histograms, scatter plots, box plots, and heatmaps.
Visualizations render complex data more interpretable, enabling analysts to present findings in an effective manner to stakeholders.
Outlier Detection: EDA identifies outliers, which are observations that deviate dramatically from the norm.
Outliers can significantly affect statistical analyses and modeling, so their identification is important.
Pattern Discovery: EDA reveals implicit patterns, relationships, and trends in the data. Data Analytics Course in Pune
These revelations can drive additional analyses and inform decision-making activities.
Assumptions Testing: EDA helps ensure or refute assumptions that were made concerning the data and the problem under consideration.
This is particularly critical in statistical modeling and analysis.
Data-Driven Decision-Making: EDA enables organizations to make data-driven decisions.
Through detailed exploration of the data, organizations can derive actionable insights and drive strategic decisions.