Classroom Training

Analyst Bootcamp

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The Analyst Bootcamp takes students through foundational concepts to the skills needed to tackle more advanced analysis and data preparation challenges. This five-day course series is built for those looking to hone their Tableau skills quickly, and covers Tableau Desktop and Prep Builder concepts, techniques, and activities to ensure a comprehensive, in-depth learning experience.

Audience: This bootcamp is designed for those in analyst roles or those looking to improve their analysis skills in Tableau to presents data insights across lines of business to improve decision making.

Prerequisites: While there are no prerequisites for this bootcamp, the rapid pace of this class favors those students who learn new concepts quickly or have previous experience with data analysis and business intelligence.

Learning Objectives: You'll learn how to:

  • Build advanced chart types and visualizations.
  • Build complex calculations to manipulate your data.
  • Use statistical techniques to analyze your data.
  • Use parameters and input controls to give users control over certain values.
  • Implement advanced geographic mapping techniques and use custom images and geocoding to build spatial visualizations of non-geographic data.
  • Prep your data for analysis.
  • Combine data sources using data blending.
  • Make your visualizations perform as well as possible using the Data Engine, extracts, efficient connection methods, and data connection best practices.
  • Build better dashboards using techniques for guided analytics, interactive dashboard design, and visual best practices.
  • Implement efficiency tips and tricks.
  • What data Tableau Desktop works well with, and how to shape data appropriately to get maximum flexibility from Tableau Desktop.
  • Understand Tableau Prep Builder’s role in the Analytic Cycle.
  • Use the terminology specific to Tableau Prep Builder.
  • Data sampling in Prep.
  • Create and understand flows that address common scenarios that users encounter in data preparation.
  • Clean, combine, shape, and validate data.

Full course description