In this module, students learn how to transform raw data into meaningful analytical metrics using Tableau’s calculated fields and parameters. Emphasis is placed on how Tableau evaluates calculations, why context and granularity matter, and how small differences in logic can dramatically change analytical results. The module also introduces core structural tools students need to apply calculations effectively in practice - including multi-measure views, basic formatting and labeling, and essential data-preparation functionality. By the end of this module, students move beyond “drag-and-drop” analysis and begin to reason explicitly about calculation scope, granularity, and analytical intent-skills that are foundational for table calculations, LOD expressions, and advanced analytics later in the course.

In this module, you will learn how data is structured, combined, and reshaped for visual analytics. The emphasis is on Tableau’s data model, including relationships, joins, and unions, as well as foundational data preparation concepts such as tidy data, pivoting, and splits. The module also introduces Tableau Prep Builder as a dedicated environment for cleaning, reshaping, and combining raw data before analysis. By the end of the module, learners will be able to prepare analysis-ready data that supports accurate and scalable visual analytics.

This module focuses on the technical construction of dashboards in Tableau. Learners develop fluency with dashboard structure and layout fundamentals, including how worksheets translate into dashboards, how Tableau manages space through tiled and floating objects, as well as container logic, object hierarchy, layout management, and interactive actions.

This module introduces table calculations, which operate on the results of a visualization rather than on the underlying data, enabling analysts to compute differences, percent changes, running totals, rankings, and window-based metrics directly within a visualization. Unlike calculated fields, table calculations are sensitive to visual layout, ordering, and direction, making them both powerful and error-prone if misunderstood. You will learn how Tableau “looks across” a table to identify analytical patterns - particularly over time. The module also introduces date manipulation functions: DATEPART, DATEADD, DATETRUNC, and DATEDIFF.

This module focuses on segmentation—how analysts define, compare, and interactively explore meaningful subsets of data. You will distinguish between tools that organize data for readability (Groups) and tools that define population structure (Sets). You will learn how population definition affects benchmarking, part-to-whole analysis, and interpretation. This module bridges view-dependent analysis (Module 4) and view-independent logic (Module 8: Level of Detail expressions).

This module introduces Level of Detail expressions as a way to control where a calculation is computed in Tableau. Rather than relying only on the level of detail present in the view, learners use LOD expressions to define the appropriate level of analysis for the question they are trying to answer. The module begins by showing why standard aggregation and table calculations can produce unstable or misleading results when the view changes. It then develops FIXED LOD expressions as a tool for creating stable denominators, benchmarks, and hidden-grain calculations. Finally, learners use INCLUDE and EXCLUDE expressions to modify the level of computation relative to the view and apply LOD logic in comparative, parameter-driven analytical patterns. This structure gives learners both a conceptual foundation and practical experience building defensible calculations in Tableau.

This module extends students’ prior work with geographic mapping by focusing on more advanced spatial design and analytical integration. Rather than revisiting traditional map construction, the module examines when geography adds analytical value, how alternative map forms such as trellis maps and stylized maps can improve comparison and dashboard usability, and how spatial logic can be used to answer questions involving distance, proximity, and coverage. The module concludes by bringing spatial and non-spatial views together in integrated dashboards, helping learners design analytical systems in which maps support interpretation rather than simply display location.

This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.