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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

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