Course Introduction
The Tableau Prep for Data Cleaning and Transformation Training Course provides comprehensive, hands-on training for professionals who need to prepare reliable, analysis-ready data for Tableau dashboards, reports, and business intelligence solutions. Tableau Prep Builder is designed to combine, shape, and clean data through visual flow-based workflows, making it particularly useful for organizations working with multiple files, databases, extracts, and inconsistent operational datasets.
This course develops practical expertise in identifying data-quality problems, correcting inconsistent values, standardizing field structures, handling missing information, changing data types, splitting and grouping values, filtering records, and removing unnecessary fields. Participants learn how to use Tableau Prep's visual profile and cleaning capabilities to understand data before transformation, while developing repeatable preparation processes that can support reliable downstream analysis.
A major focus of the training is combining datasets correctly using joins, unions, pivots, and aggregations. Participants learn how to select appropriate join fields, identify duplication caused by mismatched granularity, reshape wide and long datasets, consolidate files, and aggregate data to an appropriate level of detail. Tableau specifically positions Prep Builder for involved preparation tasks such as multiple joins, field cleanup, data-type changes, and multiple pivots, making these capabilities central to professional data preparation workflows.
The course also covers calculated fields, data validation, data roles, reusable parameters, and advanced transformation techniques. Participants learn how calculations can create derived fields and support more complex transformations, while data roles can help identify values that do not conform to recognized domains or patterns. Tableau Prep also supports FIXED level-of-detail, ranking, row-number, and tile calculations, giving experienced users additional techniques for solving preparation and data-quality challenges.
Automation and reusable data-preparation workflows form another important component of the program. Participants learn how to design readable flow structures, document transformation logic, parameterize reusable workflows, create appropriate outputs, and prepare data for Tableau analysis or publication. Tableau Prep parameters can be applied to areas such as file paths, table names, filter expressions, and calculated fields, while flow outputs can be generated as extracts, published data sources, or database outputs depending on the working environment.
The training also introduces current productivity capabilities, including Tableau Agent in supported Tableau Prep environments, while emphasizing that automated assistance should be reviewed and validated against business and data-quality requirements. Tableau documentation states that Tableau Agent can assist with cleaning, transformation, flow building, and calculated fields in supported versions. By the end of the course, participants will be able to design structured Tableau Prep flows that transform messy source data into consistent, validated, reusable datasets suitable for analytics and visualization.
Duration
5 days
Who Should Attend
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Data analysts who need to clean and transform datasets before developing Tableau dashboards and reports.
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Business intelligence professionals responsible for preparing reliable analytical datasets from multiple operational sources.
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Tableau developers who want to move complex data preparation activities into structured Tableau Prep workflows.
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Reporting specialists who regularly encounter inconsistent field names, formats, values, data types, and missing records.
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Data engineers who want to use Tableau Prep for selected self-service and analyst-oriented transformation workflows.
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Business analysts working with spreadsheets, databases, extracts, and other heterogeneous sources requiring systematic preparation.
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Analytics professionals responsible for combining, reshaping, aggregating, and validating data before visualization.
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Data quality professionals who need practical methods for identifying and correcting common data-quality issues.
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Tableau administrators and platform teams supporting governed data-preparation and publishing processes.
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Finance, sales, marketing, operations, and customer analytics professionals preparing departmental datasets for Tableau analysis.
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Professionals migrating manual spreadsheet-based cleaning processes into repeatable visual data-preparation workflows.
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Anyone seeking practical, hands-on training in Tableau Prep Builder for professional data cleaning and transformation.
Course Objectives
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Develop practical proficiency in Tableau Prep Builder for connecting to, profiling, cleaning, transforming, combining, validating, and preparing datasets for analytical use.
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Identify common data-quality problems involving missing values, duplicates, inconsistent formats, incorrect data types, invalid values, and poorly structured source fields.
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Build structured Tableau Prep flows that use appropriate cleaning, filtering, splitting, grouping, renaming, and field-management techniques for repeatable preparation.
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Combine multiple datasets using joins, unions, pivots, and aggregations while controlling granularity and reducing the risk of duplicated or misleading analytical results.
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Create calculated fields and advanced transformation logic to derive useful analytical attributes and resolve business-specific data-preparation requirements.
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Apply data roles and validation techniques to identify invalid or inconsistent values and establish more reliable standards for analytical datasets.
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Use parameters and reusable flow designs to support flexible preparation scenarios where similar transformation logic must be applied to changing source data.
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Design efficient and maintainable flows with clear sequencing, documentation, appropriate levels of detail, and practical troubleshooting techniques.
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Produce clean and analysis-ready outputs that can be used in Tableau Desktop, Tableau Cloud, Tableau Server, extracts, published data sources, or supported database workflows.
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Complete a practical data-preparation project that integrates multiple source datasets, resolves quality issues, applies transformations, validates results, and produces a production-oriented output.
Comprehensive Course Outline
Module 1: Introduction to Tableau Prep and Data Preparation
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Understanding the role of Tableau Prep Builder within modern analytics, business intelligence, and Tableau-based data preparation workflows.
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Exploring the Tableau Prep workspace, including connections, flow pane, profile pane, data grid, transformation steps, and output configuration.
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Understanding the data-preparation lifecycle from source connection and profiling through transformation, validation, execution, and analytical consumption.
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Designing a structured preparation strategy that separates source understanding, cleaning, transformation, combination, validation, and output activities.
Module 2: Connecting to and Profiling Source Data
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Connecting Tableau Prep to files, databases, Tableau extracts, and other supported sources while evaluating the structure of incoming datasets.
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Examining field distributions, data types, values, nulls, and anomalies through Tableau Prep's visual profiling and data exploration capabilities.
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Identifying source-system inconsistencies such as unexpected formats, duplicated records, incorrect field types, missing values, and inconsistent naming conventions.
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Establishing data-quality checkpoints that determine which problems should be corrected during preparation and which require attention at the source system.
Module 3: Data Cleaning and Standardization Techniques
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Applying filters, field removal, renaming, duplication, splitting, grouping, and value-editing techniques to improve dataset consistency and usability.
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Standardizing categorical values, text fields, date representations, numerical formats, and business terminology across heterogeneous source datasets.
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Managing null values, invalid records, duplicate information, inconsistent spelling, unexpected characters, and other common data-quality challenges.
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Using profile results and the data grid to inspect transformation outcomes and verify that cleaning actions produce the intended results.
Module 4: Combining Data with Joins and Unions
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Designing reliable joins using appropriate keys while understanding how one-to-one, one-to-many, and many-to-many relationships can affect resulting data.
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Diagnosing duplicate records, unmatched records, null join results, and unexpected row multiplication caused by inappropriate join conditions or granularity.
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Building unions to consolidate similarly structured datasets and developing strategies for handling recurring files and evolving source structures.
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Comparing join and union approaches and selecting the appropriate combination method based on business requirements, data structure, and analytical objectives.
Module 5: Pivoting, Aggregation, and Reshaping Data
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Converting wide datasets into analytical structures using pivot operations and understanding when row-to-column or column-to-row reshaping is appropriate.
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Aggregating data to appropriate levels of detail while selecting suitable grouping dimensions and measures for downstream analytical requirements.
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Managing granularity before joins and transformations to reduce duplication and create consistent structures for analysis.
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Building transformation flows that combine pivot, aggregation, and cleaning operations to convert operational datasets into analytics-ready structures.
Module 6: Calculations and Advanced Data Transformation
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Creating calculated fields to derive business attributes, standardize values, perform conditional transformations, and support analytical preparation requirements.
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Applying FIXED level-of-detail calculations to perform transformations at defined levels of granularity within Tableau Prep workflows.
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Using row-number, ranking, and tile calculations to identify duplicates, segment records, rank values, and create structured analytical groupings.
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Developing calculation logic that is readable, maintainable, appropriately documented, and aligned with the intended business definition of each derived field.
Module 7: Data Validation, Quality Control, and Governance
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Using Tableau Prep data roles to identify invalid values and validate fields against recognized patterns, domains, geographic values, URLs, emails, and other standards.
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Establishing practical data-quality checks for completeness, consistency, validity, uniqueness, accuracy, and conformity before datasets reach analytical users.
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Creating repeatable validation processes that identify problems early and document the corrective actions applied during preparation.
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Designing preparation workflows that improve trust in analytical datasets while maintaining clear transformation logic and traceability throughout the flow.
Module 8: Parameters, Reusable Flows, and Automation
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Creating reusable Tableau Prep workflows that can accommodate changing input scenarios without rebuilding the same transformation logic repeatedly.
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Applying user parameters to supported file paths, table names, filter expressions, calculated fields, and other configurable flow elements.
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Designing parameter-driven preparation processes for recurring regional, departmental, monthly, or operational datasets with consistent schemas.
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Configuring output strategies and preparing flows for repeatable execution, published data sources, extracts, and database-based analytical workflows.
Module 9: Flow Optimization, Troubleshooting, and Modern Capabilities
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Organizing complex flows with logical step sequences, meaningful descriptions, readable branches, and clear transformation stages for long-term maintainability.
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Troubleshooting unexpected results by tracing transformations, reviewing profiles, checking join conditions, examining calculations, and validating intermediate outputs.
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Evaluating flow design decisions that can affect processing efficiency, data volume, transformation complexity, and maintainability across recurring workflows.
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Exploring Tableau Agent in supported environments as an assistance capability for cleaning, transformation, flow construction, and calculated-field development while validating generated results.
Module 10: End-to-End Tableau Prep Data Transformation Project
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Building a complete Tableau Prep flow that connects multiple source datasets, profiles data quality, cleans inconsistent records, and establishes standardized structures.
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Combining datasets through appropriate joins and unions, reshaping information with pivots, and aggregating records to analytically meaningful levels of detail.
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Applying calculated fields, validation techniques, quality checks, and reusable parameters to create a robust and maintainable preparation workflow.
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Publishing or generating the final analysis-ready dataset and presenting the completed flow, transformation decisions, validation results, and recommended production practices.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
