Data cleaning and preparation are essential steps in the data analytics process. Analysts begin by inspecting the dataset for missing, inconsistent, or duplicate values. They handle missing data by either removing rows, filling with statistical values (mean, median), or using predictive imputation methods. Duplicate entries are identified and removed to ensure data integrity.

Next, they standardize data formats for example, ensuring all dates follow a single format or converting text to lowercase for consistency. Data might also be normalized or scaled, especially for algorithms sensitive to magnitude, such as clustering or regression models.

Categorical variables are encoded using techniques like one-hot encoding or label encoding. Analysts also examine outliers, which may indicate errors or important anomalies, depending on the context. Feature engineering may be performed to create new, more meaningful variables from existing data to improve model performance.

Throughout the process, analysts use tools like Python (with pandas, NumPy), Excel, or platforms like SQL to manipulate and clean the data. This process improves the quality, consistency, and reliability of insights drawn from the data, making it a cornerstone of accurate data-driven decision-making.

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