Data Wrangling & Preparation Certification

Module 1: Introduction to Data Wrangling

• Data wrangling fundamentals
• Data preparation lifecycle
• Raw versus processed data
• Common data challenges
• Data quality concepts
• Analytics workflows

Module 2: Understanding Data Structures

• Structured data
• Semi-structured data
• Unstructured data
• Tabular datasets
• JSON data
• Data schemas

Module 3: Python for Data Preparation

• Python fundamentals
• Data structures
• Functions
• File handling
• Iteration
• Data processing workflows

Module 4: NumPy for Data Manipulation

• NumPy arrays
• Indexing and slicing
• Array operations
• Reshaping
• Broadcasting
• Numerical transformations

Module 5: Pandas Fundamentals

• Series
• DataFrames
• Importing data
• Selecting records
• Filtering
• Sorting

Module 6: Data Profiling

• Dataset inspection
• Data types
• Summary statistics
• Unique values
• Frequency analysis
• Data quality assessment

Module 7: Handling Missing Data

• Missing-value identification
• Removing missing data
• Imputation techniques
• Conditional replacement
• Missing-data patterns
• Validation

Module 8: Duplicate Data Management

• Duplicate detection
• Exact duplicates
• Partial duplicates
• Duplicate removal
• Record comparison
• Data consistency

Module 9: Data Type Conversion

• Numeric conversion
• String conversion
• Date and time conversion
• Categorical data
• Boolean data
• Conversion errors

Module 10: Cleaning Text Data

• String operations
• Whitespace removal
• Case standardization
• Pattern replacement
• Regular expressions
• Text normalization

Module 11: Handling Outliers

• Outlier identification
• Statistical methods
• IQR method
• Z-score concepts
• Outlier treatment
• Business-rule validation

Module 12: Data Transformation

• Mapping
• Applying functions
• Binning
• Scaling
• Normalization
• Transformation workflows

Module 13: Combining Datasets

• Concatenation
• Joins
• Merges
• Lookup operations
• Key management
• Relationship validation

Module 14: Reshaping Data

• Pivoting
• Melting
• Stacking
• Unstacking
• Wide-to-long conversion
• Data restructuring

Module 15: Aggregation & Grouping

• GroupBy operations
• Aggregations
• Multiple aggregations
• Summary tables
• Conditional aggregation
• Analytical preparation

Module 16: Date & Time Data Preparation

• Date parsing
• Time components
• Date calculations
• Time intervals
• Time-series preparation
• Temporal features

Module 17: SQL for Data Preparation

• Data extraction
• Filtering
• Joins
• Aggregations
• CASE expressions
• Data transformation queries

Module 18: Feature Preparation for Machine Learning

• Feature selection
• Categorical encoding
• Feature scaling
• Normalization
• Derived features
• Dataset preparation

Module 19: Data Validation & Quality Checks

• Validation rules
• Range checks
• Format checks
• Consistency checks
• Referential integrity
• Quality reporting

Module 20: Automating Data Preparation

• Reusable functions
• Processing pipelines
• Batch preparation
• Workflow automation
• Error handling
• Logging concepts

Module 21: Advanced & Scalable Data Wrangling

• Large dataset challenges
• Memory optimization
• Efficient Pandas operations
• Chunk processing
• Performance optimization
• Scalable preparation concepts

Module 22: Practical Data Wrangling Project

• Raw data assessment
• Data profiling
• Data cleaning
• Dataset integration
• Transformation
• Validation and final output