Data Cleaning and Transformation
Bachelor of Science in Computer Science · University · Big Data Analytics
This unit introduces data cleaning techniques to handle missing values, outliers, and inconsistencies. It also covers data transformation methods for feature engineering, enabling students to prepare data for effective analysis in big data applications.
Zum Üben gibt es 41 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Missing Values: Types
- Imputation Techniques
- Outliers: Detection
- Treatment Methods
- Data Transformation: Normalization
- Standardization
- Binarization
- Feature Engineering: Feature Selection
- Feature Creation
- Data Cleaning and Transformation in Big Data: Challenges
- Best Practices
Stichworte
- Data Cleaning
- Data Transformation
- Missing Values
- Outliers
- Inconsistencies
- Feature Engineering
- Big Data Analytics