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Duration 14 hours
Course Outline
Introduction to Alteryx and the Designer Environment
- Overview of the Alteryx Designer interface and workflow canvas
- Setting up workflows, tool palettes, and workflow properties
- Best practices for saving, documenting, and sharing workflows
Core Data Preparation Tools
- Input Data and Output Data tools: connecting to CSV, Excel, and databases
- Select, Filter, Sort, and Browse tools for rapid data inspection and refinement
- Practical exercises: cleansing a sample dataset
Foundational Data Transformation
- Using the Formula tool for calculated fields and conditional logic
- Data cleansing: managing nulls, trimming whitespace, and standardising values
- Text to Columns and parsing delimited fields
Simple Data Combination
- Join and Union tools for merging datasets
- Summarize tool for aggregation and roll-ups
- Hands-on: building an end-to-end ETL workflow
Advanced Data Blending and Parsing (Intermediate)
- Effectively blending multiple data sources and file formats
- Parsing semi-structured data: fundamentals of XML and JSON
- Techniques for validating and normalising blended data
Basic Analytical Tools and Reporting
- Find Replace, Cross Tab, and Transpose for reshaping data
- Generating simple reports and exporting results
- Case study: creating a summarised operational report
Introduction to Macros and Reusability
- Types of macros: Standard Macros and use cases
- Creating, testing, and packaging a reusable macro
- Implementing macros within workflows to simplify processes
Workflow Automation Best Practices
- Organising workflows using containers and annotations
- Considerations for error handling, logging, and scheduling
- Practical exercise: automating a recurring data preparation task
Summary and Next Steps
Requirements
- A solid grasp of fundamental data concepts and spreadsheet usage
- Proficiency with CSV and Excel file formats
- Foundational analytical and problem-solving skills
Target Audience
- Data analysts and business analysts
- ETL practitioners and operational staff
- Professionals responsible for automating routine data tasks