Course Outline
Introduction
Overview of Alteryx
- What is Alteryx?
- Key features of Alteryx
Preparing the Development Environment
- Installing and configuring Alteryx
Input and Reading Data
- Connecting data sources to Alteryx
- Importing data
- Using the Input tool
Data Wrangling
- Using data preparation and parsing tools
- Working with joins and unions
Transformations
- Applying various pivot methods
- Aggregating data
Layouts
- Designing reporting layouts
- Exporting reports
Summary and Conclusion
Requirements
- Understanding of data analysis concepts
Target Audience
- Data Analysts
Testimonials (7)
Abhi has excellent knowledge of Alteryx and he explained things very clearly. He understood our goals and created bespoke demo datasets that were relevant to our organisation, which was very impressive. The training was well-structured and delivered at a good pace, with time for questions.
Samuel Taylor - Manchester Metropolitan University
Course - Alteryx for Data Analysis
Trainer was very good at explaining concepts and tools
Caroline Colfer - Manchester Metropolitan University
Course - Alteryx for Data Analysis
Use cases were awesome! and Ray involved each and every one of us in each use case.
Zara - Trench Ltd
Course - Alteryx for Data Analysis
Provide slides, videos, etc in advance.
Natalia - Trench Ltd
Course - Alteryx for Data Analysis
Ray was really knowledgeable and also really enthusiastic. He was able to hold my attention. The training plan was logical and he was able to adapt it to answer specific questions. He was easy to understand and was able to simplify some complex topics. A great trainer!
Lillian - Trench Ltd
Course - Alteryx for Data Analysis
How there was time given to work through examples of issues we were facing on the ground.
Thomas Bryson - NHS England
Course - Alteryx for Data Analysis
Trainer flexibility in the following areas: 1. Shifting the agenda to address some questions I had which required the introduction of some Alteryx tools which Pedro had initially planned to introduce later. 2. Accommodating actual use cases in our hands-on exercise. 3. Tweaking of the agenda to include some machine learning concepts albeit at a high level.
