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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
- Configuration of workflows, tool palettes, and workflow properties
- Best practices for saving, documenting, and sharing workflows
Essential Data Preparation Tools
- Input Data and Output Data tools: connecting to CSV, Excel, and database sources
- Using Select, Filter, Sort, and Browse for rapid data inspection and pruning
- Practical exercises: cleansing a sample dataset
Basic Data Transformation
- The Formula tool for creating calculated fields and applying conditional logic
- Data Cleansing: managing nulls, trimming, and standardizing values
- Text to Columns and parsing delimited fields
Simple Data Combination
- Join and Union tools for merging datasets
- Summarize for performing aggregation and roll-ups
- Hands-on activity: constructing 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 normalizing blended data
Basic Analytical Tools and Reporting
- Find Replace, Cross Tab, and Transpose for reshaping data structures
- Generating simple reports and exporting results
- Case study: producing a summarized operational report
Introduction to Macros and Reusability
- Types of macros: Standard Macros and appropriate use cases
- Creating, testing, and packaging a reusable macro
- Deploying macros within workflows to simplify processes
Workflow Automation Best Practices
- Organizing 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 fundamental understanding of basic data concepts and spreadsheets
- Experience with CSV and Excel file formats
- Basic analytical thinking and problem-solving capabilities
Target Audience
- Data analysts and business analysts
- ETL practitioners and operations team members
- Individuals responsible for automating routine data tasks