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This course now features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

This comprehensive course is designed to prepare you for the Tableau Desktop Specialist Exam while equipping you with practical, hands-on skills to master Tableau. By the end of the course, you will be able to visualize, analyze, and share data with confidence, boosting your career with a valuable certification. You will gain a deep understanding of Tableau's functionality, from basic visualizations to advanced data analysis.

You will start by installing Tableau Public and getting familiar with its user interface. As you progress, you will explore fundamental Tableau concepts such as dimensions, measures, aggregation, and granularity. With these foundations, you'll dive into a variety of data visualizations including bar charts, line charts, scatter plots, and advanced charts like dual-axis and stacked bar charts. You'll also learn how to organize and manipulate data to extract actionable insights, using features like filters, groups, sets, and calculated fields.

The course will guide you through advanced techniques, such as table calculations, trend lines, and using parameters for interactive dashboards. You will also learn how to format and share your work, including exporting to PDF, PowerPoint, and publishing to Tableau Server. Along the way, you’ll develop the skills necessary to prepare and clean data, create live and extract connections, and optimize your Tableau visualizations for different devices.

This course is perfect for anyone seeking to become proficient in Tableau and pass the Tableau Desktop Specialist exam. It is ideal for beginners with no prior experience in Tableau but is also suitable for intermediate learners who want to refine their skills. A basic understanding of data analysis concepts is helpful, but no prior Tableau knowledge is required.

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Syllabus

Introduction
In this module, we will introduce the course layout, cover key topics essential for the Salesforce Certified Tableau Desktop Foundations Exam, and familiarize you with Tableau’s capabilities. You’ll also learn how to install Tableau Public and explore its interface and features for navigating the software efficiently.
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Career center

Learners who complete Ace Salesforce Certified Tableau Desktop Foundations Exam will develop knowledge and skills that may be useful to these careers:
Data Analyst
A Data Analyst transforms raw data into understandable insights, often using tools like Tableau to create compelling visualizations and interactive dashboards. This course directly equips you with the foundational skills to excel as a Data Analyst. By mastering Tableau's functionalities, from handling dimensions and measures to creating advanced charts and applying table calculations, you will be able to dissect complex datasets and present actionable findings. The ability to prepare and clean data, create live and extract connections, and optimize visualizations for various devices, as taught in this course, is paramount for a Data Analyst to efficiently manage and visualize data. Furthermore, learning to design and format dashboards and share insights effectively ensures you can communicate your analyses clearly to stakeholders.
Data Visualization Specialist
A Data Visualization Specialist focuses on creating clear, compelling, and insightful visual representations of data to help audiences understand complex information quickly. This course is an ideal pathway to becoming a highly proficient Data Visualization Specialist. You will gain extensive hands-on experience in generating a wide array of charts, from fundamental bar and line charts to more intricate dual-axis and stacked bar charts. The course emphasizes organizing and manipulating data using filters, groups, sets, and calculated fields, which are essential for presenting data effectively. Moreover, the focus on designing and formatting interactive dashboards, optimizing views for different devices, and exporting work to various formats directly equips you with the practical skills needed to communicate data visually and impactfully in this specialized role.
Business Intelligence Analyst
A Business Intelligence Analyst plays a crucial role in transforming data into strategic business insights, often by designing and implementing dashboards and reports. The "Ace Salesforce Certified Tableau Desktop Foundations Exam" course is exceptionally well-suited for aspiring Business Intelligence Analysts as it provides a comprehensive foundation in Tableau. You will develop critical skills in visualizing, analyzing, and sharing data, which are central to this role. The course's focus on connecting to and preparing data, creating various data visualizations including advanced charts, and designing interactive dashboards tailored for different devices directly aligns with the responsibilities of a Business Intelligence Analyst. Learning to publish to Tableau Server and share insights in formats like PDF and PowerPoint further enhances your ability to communicate key business trends and performance indicators to decision-makers.
Customer Insights Analyst
A Customer Insights Analyst specializes in interpreting customer data to understand behaviors, preferences, and trends, providing strategic recommendations to improve customer experience and drive business growth. This course is highly relevant for a Customer Insights Analyst, as it thoroughly trains you in Tableau, a powerful tool for customer data visualization. You will develop expertise in connecting to and preparing various customer datasets, such as demographics, purchasing history, and interaction logs. The course's emphasis on creating diverse data visualizations, applying filters and sets to segment customers, and designing interactive dashboards tailored for different devices directly supports the core functions of a Customer Insights Analyst in uncovering valuable customer patterns and communicating compelling narratives about your customer base.
Reporting Analyst
A Reporting Analyst is responsible for compiling, analyzing, and presenting data in regular reports that inform business operations and strategic decisions. This course provides an excellent foundation for a career as a Reporting Analyst by thoroughly preparing you to create professional and insightful reports using Tableau. You will learn to connect to and prepare data from various sources, apply fundamental Tableau concepts like dimensions and measures, and create a diverse range of visualizations including combined axis and stacked bar charts. The course module on "Sharing Insights" is particularly relevant, covering dashboard design, interactive actions, and optimizing views, as well as exporting reports to PDF and PowerPoint. These skills are essential for a Reporting Analyst to effectively communicate key metrics and trends to stakeholders.
Healthcare Data Analyst
A Healthcare Data Analyst collects, processes, and analyzes large datasets related to patient care, public health, and healthcare operations to identify trends, improve outcomes, and inform decision-making. This course provides robust data visualization and analysis skills using Tableau, which can significantly benefit a Healthcare Data Analyst. You will gain proficiency in connecting to and preparing diverse healthcare datasets, from electronic health records to claims data, and apply fundamental Tableau concepts to uncover critical insights. The ability to create various visualizations—such as bar charts for comparing patient outcomes, or line charts for tracking disease prevalence—and design interactive dashboards with parameters, is crucial for monitoring healthcare performance and communicating findings to clinicians, administrators, and policymakers. This course helps build a foundation for effectively managing and presenting complex healthcare data.
Analytics Consultant
An Analytics Consultant advises organizations on leveraging data for strategic insights and often implements analytical solutions, including designing and deploying data visualization tools. This course can serve as a strong entry point for aspiring Analytics Consultants by providing expert-level proficiency in Tableau. Consultants frequently need to quickly understand client data, visualize key performance indicators, and present actionable recommendations. The course covers connecting to and preparing data, exploring various visualization techniques, creating interactive dashboards with parameters, and publishing to Tableau Server – all crucial skills for an Analytics Consultant to deliver impactful data-driven solutions. The ability to share insights effectively in various formats further ensures you can communicate findings clearly to diverse client audiences.
Researcher Data Specialist
A Researcher Data Specialist manages, analyzes, and visualizes data for research projects, often in academic, scientific, or social science contexts. This course provides excellent preparation for a Researcher Data Specialist by equipping you with comprehensive skills in Tableau, a versatile tool for presenting research findings. You will learn to connect to and prepare diverse research datasets, apply fundamental Tableau concepts like aggregation and granularity, and create a wide array of visualizations, including scatter plots for correlations and line charts for temporal trends. The ability to design interactive dashboards with parameters allows for dynamic exploration of research data, while the skills in exporting to formats like PDF and PowerPoint efficiently support the dissemination of research results to colleagues and scientific communities.
Product Analyst
A Product Analyst focuses on understanding product performance, user engagement, and market needs through data analysis to inform product development and strategy. This course provides highly relevant skills for a Product Analyst by enabling proficiency in Tableau for visualizing and interpreting product-related data. You will learn to connect to and prepare various product datasets, from user telemetry to feature adoption rates, and apply fundamental Tableau concepts to explore and analyze this information. The ability to create advanced charts, track trends, and design interactive dashboards with parameters, as taught in this course, is crucial for a Product Analyst to monitor product health, identify user pain points, and communicate data-driven insights to product managers and development teams.
Sales Analyst
A Sales Analyst evaluates sales data, identifies trends, tracks performance against targets, and provides insights to optimize sales strategies. This course can significantly benefit a Sales Analyst by providing comprehensive skills in Tableau, a powerful tool for sales data visualization and reporting. You will gain expertise in connecting to and preparing diverse sales datasets, from customer demographics to transaction histories, and applying Tableau’s functionalities to analyze sales performance. Creating various charts, such as bar charts to compare regional sales or line charts to show sales growth over time, and designing interactive sales dashboards with parameters are all skills directly applicable to this role. The course’s emphasis on sharing insights ensures a Sales Analyst can effectively communicate findings and recommendations to the sales team and management.
Marketing Analyst
A Marketing Analyst interprets market data, customer behavior, and campaign performance to inform marketing strategies and measure effectiveness. This course can significantly enhance your capabilities as a Marketing Analyst. By mastering Tableau, you gain the ability to visualize complex marketing datasets, such as website traffic, conversion rates, and campaign ROI, through various charts and interactive dashboards. The skills in preparing and cleaning data, understanding dimensions and measures, and creating trend lines are particularly beneficial for identifying patterns and insights in marketing data. Learning to design and format dashboards and export insights to PowerPoint, as covered in this course, empowers a Marketing Analyst to effectively communicate performance metrics and strategic recommendations to marketing teams and leadership.
Financial Analyst
A Financial Analyst evaluates financial data, market trends, and economic conditions to provide insights for investment decisions, budgeting, and forecasting. While this role typically involves specialized financial knowledge, this course may be useful for a Financial Analyst by providing robust data visualization and analysis skills using Tableau. The ability to connect to and prepare diverse financial datasets, visualize key metrics like revenue and expenditure trends using line charts, and create interactive dashboards to monitor financial performance are highly relevant. Understanding aggregation, granularity, and applying table calculations—all taught in this course—helps a Financial Analyst dissect financial statements and present complex information clearly, enhancing their capacity to communicate financial health and forecasts to stakeholders.
Operations Analyst
An Operations Analyst examines an organization's processes and workflows to identify inefficiencies, optimize performance, and improve productivity. This course may be helpful for an Operations Analyst by equipping them with the essential skills to visualize and analyze operational data effectively using Tableau. The ability to connect to and prepare various data sources, such as production logs or supply chain metrics, and then apply fundamental Tableau concepts like dimensions and measures, allows for deep operational insight. Creating charts like bar charts to compare performance across different operational units, or line charts to track process efficiency over time, and designing interactive dashboards empowers an Operations Analyst to monitor key performance indicators and present data-driven recommendations for process improvements.
Management Consultant
A Management Consultant advises organizations on improving their efficiency and solving complex business challenges across various functions. This course may be helpful for a Management Consultant due to its focus on data visualization and analysis using Tableau. Consultants frequently encounter diverse datasets from clients and must quickly transform them into actionable insights and compelling presentations. The ability to connect to and prepare data, explore various visualization techniques, and design interactive dashboards, as covered in this course, can significantly enhance a Management Consultant's capacity to analyze client situations, identify key trends, and communicate data-driven recommendations effectively to senior leadership. Sharing insights through professional exports also aligns with common consulting deliverables.
Data Scientist
A Data Scientist extracts knowledge and insights from data using statistical methods, machine learning, and programming, often presenting complex findings visually. While this role typically requires an advanced degree and expertise in advanced analytics, this course may be useful for an aspiring Data Scientist by building a strong foundation in data preparation and visualization using Tableau. Data Scientists spend considerable time connecting to, cleaning, and exploring data before modeling. The course's modules on preparing and cleaning data, understanding dimensions and measures, and creating diverse data visualizations are directly applicable. Learning to design and share interactive dashboards can also help a Data Scientist effectively communicate preliminary findings and model results to non-technical stakeholders.

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Ace Salesforce Certified Tableau Desktop Foundations Exam.
Seminal work on the grammar of graphics, a formal language for describing data visualizations. It covers a wide range of topics, from the basics of the grammar of graphics to the use of the grammar of graphics to create a variety of charts and graphs.
This classic book by Edward Tufte must-read for anyone interested in data visualization. It covers a wide range of topics, from the principles of visual perception to the design of effective charts and graphs.
Provides a definitive guide to Hadoop, covering topics such as data storage, data processing, and data analysis. It valuable resource for anyone who wants to learn more about this topic.
Created by the original architect of pandas, this masterwork teaches the canonical data structures and vectorized operations essential for slicing, cleaning, merging, and reshaping complex tabular datasets in Python.
Presents the modern tidyverse grammar for transforming messy data into structured insights. Covers tibbles, dplyr pipelines, tidyr pivoting, and purrr iterations for rapid exploratory data manipulation.
Is widely considered the authoritative reference for DAX, the formula language used in Power BI, Analysis Services, and Power Pivot for Excel. It provides a comprehensive understanding of calculated fields within the Microsoft BI ecosystem, covering fundamental concepts to advanced techniques. It is an essential reference for anyone working extensively with these tools and looking to solidify their understanding of creating complex calculations.
Demystifies relational data transformation through practical PostgreSQL projects. Teaches window functions, subqueries, regular expressions, and cross-tabulations required to manipulate data directly inside the database.
Seminal work on the subject of data visualization, and it is still one of the most influential books in the field. Tufte provides a comprehensive overview of the principles of data visualization, and he covers a wide range of topics, from data types to visual perception. Tufte's work is deeply rooted in using data visualization to present only truth and avoid misrepresenting data. This book is very thorough and can be challenging for those who are new to dashboards.
Delivers a comprehensive, project-driven guide to wrangling messy timeseries, handling missing values, and restructuring dataframes with pandas. Emphasizes defensive coding and performance profiling for large analytical workloads.
Explores the mathematical and algorithmic techniques for transforming raw tabular data into predictive signals. Details numeric scaling, log transforms, categorical encoding, text vectorization, and automated feature extraction.
An excellent resource for those using R, this book provides a comprehensive introduction to data wrangling, transformation, and visualization using the tidyverse suite of packages. It fundamental text for anyone learning data science with R, covering essential data preparation steps. is often used as a textbook in introductory data science courses.
Guides engineers through extracting, cleaning, and transforming irregular data from diverse formats including JSON, XML, PDF, and messy CSVs. Focuses on automation, error detection, and parsing web feeds reliably.
Provides a deep, foundational look into R core data objects, subscripting mechanics, factor conversions, string manipulation, and reshaping vectors. Offers timeless clarity on base R data processing paradigms.
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Feature engineering critical part of data preparation for machine learning. focuses specifically on this topic, providing techniques and strategies for creating effective features from raw data. It valuable resource for anyone working on machine learning projects.
Lays out a systematic workflow for profiling, assessing, and structuring untidy business data. Connects data preparation directly to downstream analytics, preventing upstream bias and data transformation bottlenecks.
Authored by leading experts in Power BI and Power Pivot, this book focuses on data analysis within the Microsoft ecosystem. It covers the use of Power Pivot and Power BI, where calculated fields (using DAX) are central to creating analytical models. practical guide for performing data analysis and building data models, directly involving the application of calculated fields.
Comprehensive guide to data visualization, covering everything from basic principles to advanced techniques. It is written in a clear and concise style, and it is packed with examples and exercises. This book does a fantastic job of providing a more in-depth look at many of the core principles associated with dashboards and great jumping-off point for learning more about them.
Focuses on practical data preprocessing specifically for machine learning applications using popular Python libraries like scikit-learn and pandas. It's highly relevant for those preparing data for modeling and provides hands-on examples. This book is particularly useful for students and professionals in the machine learning domain.
Provides a practical guide to big data analytics, covering topics such as data mining, machine learning, and data visualization. It valuable resource for anyone who wants to learn more about this topic.
Provides a practical guide to data visualization, covering topics such as data storytelling, data exploration, and data dashboards. It valuable resource for anyone who wants to learn more about this topic.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Provides a complete guide to Apache Spark, covering topics such as data processing, data analysis, and machine learning. It valuable resource for anyone who wants to learn more about this topic.
This guidebook offers a comprehensive overview of the Business Intelligence lifecycle, from data integration to analytics. It provides clear explanations of architectural design and processes, making it valuable for gaining a broad understanding of the field. It useful reference for navigating the complexities of BI projects and includes practical solutions.
Delivers fast, battle-tested techniques for cleaning and pivoting irregular spreadsheet exports. Shows how to master Power Query, dynamic arrays, and nested lookups to clean corporate datasets with surgical speed.
Is an excellent resource for Tableau users looking to enhance their skills, including the creation and use of calculated fields within Tableau. It covers a wide range of Tableau's features, from basic concepts to advanced analytics and dashboard design. This book is valuable for both beginners and experienced professionals seeking to deepen their understanding of Tableau's capabilities for data analysis and visualization.
Presents a proactive architectural framework for monitoring data pipelines and catching silent transformation bugs. Teaches automated anomaly detection, schema drift prevention, and lineage tracking for robust data operations.
Combines practical Python data wrangling techniques with a focus on data quality. It covers using Python to read, write, and transform data while addressing quality issues. It's a practical resource for those who want to integrate data quality checks into their Python data preparation workflows.
Collects cautionary case studies from seasoned data practitioners detailing how hidden encoding bugs, missing fields, schema mismatches, and parsing failures occur, with concrete recipes for defensive data hygiene.
Offers a broader perspective on data wrangling principles beyond specific tools. It delves into the process and techniques for preparing data effectively, regardless of the software or language used. It's valuable for gaining a solid understanding of the underlying concepts of data preparation. This book is suitable for both students and professionals seeking a deeper understanding of data wrangling methodologies.
Exposes the most widespread and damaging traps in relational data manipulation and query construction. Illustrates how to avoid Cartesian explosion, improper NULL comparisons, and flawed table structures with cleaner SQL alternatives.
A more advanced book by the authors of 'The Definitive Guide to DAX,' this resource focuses on optimizing DAX expressions for performance. This is crucial for complex data models involving numerous calculated fields. It's valuable for experienced users who need to ensure their calculations are efficient and their reports are performant.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Explores advanced in-process analytical querying and columnar data manipulation using DuckDB. Teaches students how to construct complex analytical pipelines, process multiterabyte Parquet tables locally, and embed SQL transformations into Python.
Written by a Tableau Zen Master, this book offers practical tips and strategies for using Tableau effectively. It includes valuable insights into creating calculated fields and leveraging them for compelling visualizations. is particularly useful for those who want to move beyond the basics and explore more advanced techniques and best practices in Tableau.
Comprehensive guide to the entire Business Intelligence project lifecycle, from planning to deployment. It provides a structured approach to implementing BI solutions and emphasizes understanding business needs and ensuring data quality. It valuable resource for project managers and those involved in BI implementation.
Presents reusable solutions (patterns) for common DAX challenges. Understanding these patterns can significantly help in creating effective and efficient calculated fields for various scenarios in Power BI and Analysis Services. It's a valuable resource for deepening one's understanding of DAX and its practical applications.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Introduces high-performance dataframe processing using Polars and Apache Arrow memory layout. Demonstrates lazy evaluation, query optimization, and multicore parallel execution for multiterabyte data manipulation in Python.
Provides real-world examples of how companies are using big data and analytics to achieve success. It showcases Business Intelligence in action across various industries, offering practical insights and inspiration. It's valuable for understanding the practical applications of BI.
Provides a comprehensive guide to best practices for data cleaning, covering steps to take before and after data collection. It emphasizes the importance of a systematic approach to ensure data quality. It valuable resource for researchers and data practitioners focused on rigorous data handling.
Explores modern columnar in-process analytical SQL transformations using DuckDB. Teaches students how to manipulate large Parquet and CSV files with lightning-fast vectorized execution directly from Python and R environments.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
A practical guide to data visualization using Tableau, emphasizing the principles and best practices of effective data storytelling.
Addresses the importance of having a sound data strategy in the age of big data and IoT. It helps readers understand how to leverage data, analytics, and Business Intelligence to drive profitability and gain a competitive edge.
Systematizes data preparation for statistical analysis in R, emphasizing string manipulations with stringr, date transformations with lubridate, and automated web scraping for data ingestion.
Supplies rapid, practical recipes for migrating pandas workflows to blazing-fast Polars pipelines. Walks through streaming chunk processing, complex string parsing, and timeseries window calculations with minimal RAM overhead.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.

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