Data Cleaning and Data Preparation .pptx

data warehouse

1. Data Cleaning and Preparation

2. Introduction •Data cleaning and preparation is a crucial step in data science and analytics. •Ensures data quality, consistency, and accuracy. •Poor data quality can lead to incorrect insights and decision-making.

3. Importance of Data Cleaning •Enhances data accuracy and reliability. •Removes errors and inconsistencies. •Improves data analysis and model performance.

4. Common Data Quality Issues •Missing data •Duplicate records •Outliers and inconsistencies •Incorrect data formats •Data entry errors

5. Handling Missing Data • Deletion : Removing rows/columns with missing values. • Imputation : Filling missing values using mean, median, or mode. • Predictive modeling : Using algorithms to estimate missing values.

6. Removing Duplicate Data •Duplicate records can arise from multiple sources. •Methods to handle duplicates: •Identify duplicates using unique identifiers. •Remove unnecessary duplicate records.

7. Handling Outliers •Outliers can distort analysis and affect model accuracy. •Methods to detect and handle outliers: •Box plots, scatter plots, and standard deviation. •Removing or transforming extreme values.

8. Data Transformation Techniques •Normalization: Scaling data within a specific range. •Standardization: Adjusting values based on mean and standard deviation. •Encoding categorical data: One-hot encoding, label encoding.

9. •Combining data from multiple sources. •Challenges •Schema mismatches •Data redundancy Data Integration

10. Data Validation •Ensuring correctness, completeness, and consistency. •Techniques: •Cross-validation with different datasets. •Using automated validation rules.

11. Tools for Data Cleaning •Python (Pandas, NumPy, OpenRefine) •R (tidyverse, data.table) •Excel (Data Cleaning functions, Power Query)

12. Automating Data Cleaning •Benefits of automation: •Saves time and reduces errors. •Tools for automation: •ETL pipelines, SQL scripts, AI-driven cleaning methods.

13. Challenges in Data Cleaning •Handling large datasets. •Identifying hidden errors. •Ensuring consistency across different sources.

14. Conclusion •Data cleaning is essential for accurate analysis and decision-making. •It is a continuous process that ensures data integrity. •Investing time in data preparation leads to better outcomes in analytics and machine learning.