Training on Data Cleaning and Preprocessing Fundamentals

Module 1: Introduction to Data Cleaning and Preprocessing

• Importance of data quality in data analysis
• Data cleaning vs. preprocessing
• Data exploration and visualization techniques
• Case Study: Cleaning messy sales data to improve reporting accuracy
• Practical: Explore and identify data quality issues in a raw dataset

Module 2: Handling Missing Data

• Types of missing data (MCAR, MAR, MNAR)
• Handling missing data techniques (deletion, imputation, modeling)
• Case Study: Managing incomplete survey data in a development project
• Practical: Apply different techniques to handle missing values

Module 3: Outlier Detection and Treatment

• Outlier identification methods (z-score, IQR, box plots)
• Outlier treatment techniques (trimming, capping, transformation)
• Case Study: Detecting anomalies in financial transaction data
• Practical: Identify and treat outliers using statistical methods

Module 4: Data Imputation

• Imputation methods (mean, median, mode, hot deck)
• Handling categorical and numerical missing values
• Imputation evaluation
• Case Study: Improving dataset completeness through imputation techniques
• Practical: Perform and compare multiple imputation methods

Module 5: Data Standardization and Normalization

• Scaling techniques (min-max scaling, z-score standardization)
• Normalization techniques (log, power transformation)
• Impact of scaling on data analysis
• Case Study: Preparing data for machine learning models
• Practical: Apply scaling and normalization techniques to datasets

Module 6: Categorical Data Handling

• Encoding categorical variables (one-hot, label encoding)
• Handling ordinal data
• Feature creation from categorical data
• Case Study: Encoding customer data for predictive modeling
• Practical: Transform categorical variables into numerical formats

Module 7: Data Integration and Profiling

• Data integration challenges and solutions
• Data profiling techniques (quality assessment, consistency checks)
• Data merging and concatenation
• Case Study: Integrating data from multiple organizational systems
• Practical: Merge and profile datasets from different sources

Module 8: Data Discretization and Binning

• Discretization methods (equal-width, equal-frequency, clustering)
• Binning techniques (attribute-based, value-based)
• Case Study: Grouping continuous data for better interpretation
• Practical: Apply binning techniques to continuous variables

Module 9: Feature Selection and Extraction

• Feature selection techniques (filter, wrapper, embedded methods)
• Feature engineering and creation
• Dimensionality reduction
• Case Study: Selecting key features for predictive analytics models
• Practical: Perform feature selection and create new features

Module 10: Data Validation and Quality Assessment

• Data validation techniques (consistency checks, range checks)
• Data profiling reports
• Continuous data monitoring and improvement
• Case Study: Ensuring data quality in a production data pipeline
• Practical: Develop a data validation and quality assessment checklist