Introduction to Tableau

As an analyst, data preparation is the most important step for impactful analysis. Without clean data, you can lead an audience to incorrect conclusions, which can ultimately undermine your credibility and even potentially cause harm. Data cleaning is not a perfect process — a good motto for all analysts is "Question everything!" (Especially your data.) Data preparation is also an iterative process. You start by wrangling dirty data but then move on to smaller, more intentional data preparation — usually to finalize your analysis or prepare your data for an audience. Preparing data in Tableau requires a different, more design-oriented level of scrutiny when compared with file or database cleaning to finalize the data for presentation purposes. This module will teach you concepts that must be implemented in a professional environment, especially if a data visualization is intended for presentation. Remember, without clean, well-prepared data, data visualizations can point to incorrect conclusions.

As the amount of data in the world exponentially grows, the need for combining data sources becomes ever more critical. Understanding how to combine data sources opens whole new areas of study. As an analyst, your daily tasks will often include combining various data sources in search of new insightful visualizations. When it comes to connecting data sources in Tableau, the amount of data you are connecting can affect the performance of your data visualizations. Because of this, Tableau offers multiple ways to combine data that help analysts optimize workflow and create more efficient data visuals.