Data Processing and Cleaning in Data Science
Data preprocessing is a crucial step in any Data Science workflow. Raw data is often incomplete, inconsistent, or contains errors that can significantly impact the accuracy of analysis and model performance. The data cleaning process includes:
- Handling Missing Values – Using techniques like mean/mode imputation, interpolation, or dropping incomplete records.
- Removing Duplicates – Ensuring data integrity by eliminating redundant records.
- Data Transformation – Standardizing formats, normalizing numerical values, and encoding categorical variables.
- Outlier Detection – Identifying and treating anomalies using statistical methods such as Z-score, IQR, or machine learning-based anomaly detection.
- Data Integration – Merging multiple datasets from different sources while ensuring consistency.
Tools commonly used for data preprocessing include Pandas, NumPy, and Scikit-learn in Python, as well as Spark DataFrames for handling large-scale datasets.
