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Shubhra Das and Skill-Up EdTech Team

This course will teach you advanced data and analytics skills for preparing and visualizing data in Tableau and will help you to prepare for the Tableau Certified Data Analyst exam.

You will learn to assess different data sources for various scenarios, schedule data updates, create alerts and subscriptions to ensure data freshness, and integrate them with relational databases using the UI and SQL queries.

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This course will teach you advanced data and analytics skills for preparing and visualizing data in Tableau and will help you to prepare for the Tableau Certified Data Analyst exam.

You will learn to assess different data sources for various scenarios, schedule data updates, create alerts and subscriptions to ensure data freshness, and integrate them with relational databases using the UI and SQL queries.

Additionally, you will organize data using folders, sets, bins, and groups. You will learn how to prepare data by customizing fields and converting field types, and by using pivot and split features on your data.

You will transform data in Tableau Prep Builder by creating flows containing input, cleaning, and union steps. You will also create and apply calculated fields and custom calculations to your data, and use specific analytics features to enhance your visualizations.

By the end of this course, you will be proficient in using advanced data and analytics features in Tableau and will be well-prepared for the Tableau Certified Data Analyst exam, making you a valuable asset in any data-driven organization.

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What's inside

Syllabus

Working with Advanced Tableau Data Features
This module dives into advanced data functionalities in Tableau, enhancing your ability to handle complex data scenarios and maximizing the tool’s potential. It builds on foundational knowledge and introduces advanced techniques for connecting, organizing, and transforming data in Tableau.
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Traffic lights

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what should give you pause
and possible dealbreakers
Develops advanced data and analytics skills in Tableau, making learners valuable assets in data-driven organizations
Prepares learners for the Tableau Certified Data Analyst exam
Covers advanced Tableau data features, including connecting, organizing, and transforming data
Explores sophisticated calculations and robust analytics features in Tableau
Teaches techniques and functionalities not covered in basic courses
Requires learners to have foundational knowledge of Tableau

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Reviews summary

Advanced tableau skills & certification prep

According to students, this course is a highly effective resource for those aiming for the Tableau Certified Data Analyst exam, providing clear explanations and practical examples. Many found the modules on advanced calculations and Tableau Prep Builder particularly strong, with hands-on exercises reinforcing learning. While generally praised for its clarity and practical application, some learners noted that certain 'advanced' topics felt more intermediate in depth, suggesting it's ideal for those bridging from foundational knowledge to a more advanced skill set rather than true experts seeking very niche optimizations. It is largely a positively received course.
Assumes foundational Tableau knowledge from learners.
"Make sure you have foundational Tableau knowledge before starting."
"Prereqs were assumed, which is fair."
"This course is highly recommended for intermediate users looking to go advanced."
Strong coverage of advanced calculations, data prep, and Tableau Prep Builder.
"Good coverage of advanced Tableau features, especially the data preparation section."
"The overview of Tableau Prep Builder was a highlight for me."
"I particularly liked the sections on sets and groups, which can be tricky."
"This course exceeded my expectations for advanced Tableau, especially with LOD expressions."
Explanations are clear, with practical, real-world examples.
"The module on advanced calculations was particularly insightful, making complex concepts easy to grasp."
"The explanations of LOD expressions and table calculations were incredibly clear, making previously daunting topics manageable."
"The hands-on exercises were well-designed and reinforced learning effectively."
"The practical examples aligned perfectly with what I've encountered in real-world scenarios."
Highly effective for Tableau Data Analyst certification readiness.
"Absolutely essential for anyone serious about the Tableau Data Analyst exam."
"Good course for preparing for the Tableau exam."
"It covered everything needed for the data analyst certification, truly elevating my Tableau skills."
"This course definitely helps solidify understanding for anyone working with data in Tableau."
Depth of 'advanced' topics is debated, often suiting intermediate users more.
"I felt some 'advanced' topics were still quite surface-level for truly mastering them."
"I was hoping for more in-depth content on performance tuning and very complex calculations. Felt more like an intermediate course."
"The course title is a bit misleading. 'Advanced' implies a higher level of detail... not a deep dive for 'mastering'."
"It doesn't truly delve into the nitty-gritty; the depth wasn't there for 'mastering'."

Activities

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Career center

Learners who complete Mastering Advanced Data and Analytics Features in Tableau will develop knowledge and skills that may be useful to these careers:

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 Mastering Advanced Data and Analytics Features in Tableau.
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.
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 book, written by the creator of the pandas library, practical introduction to the tools needed for data manipulation, cleaning, and preparation in Python. It is highly relevant for anyone working with data in Python and serves as an excellent resource for both beginners and those looking to solidify their understanding of using pandas and NumPy for data preparation tasks. is widely used and considered a standard reference in the field.
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.
Introduces the fundamental principles of data science and data-analytic thinking from a business perspective. It helps readers understand how to extract valuable knowledge and business value from data, covering various data mining techniques without getting overly technical. Based on an MBA course, it uses real-world business problems to illustrate concepts, making it highly relevant for business-oriented individuals and professionals.
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.
Essential for anyone looking to deepen their understanding of SQL performance and optimization. explains how databases execute queries and provides insights into writing efficient SQL code. It's highly recommended for developers and database administrators aiming to improve application performance.
Focusing on the crucial aspect of communicating insights, this book teaches the fundamentals of data visualization and how to tell compelling stories with data. It provides practical guidance and real-world examples to help readers create effective visualizations and presentations. is highly recommended for anyone who needs to present data-driven findings clearly and persuasively, regardless of their technical background.
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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.
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.
Provides a broad, introductory overview of data analytics concepts, making it ideal for beginners across various disciplines. It covers key data concepts and includes real-world examples and case studies to solidify understanding. Many universities use this book as a textbook for introductory data analytics courses. It serves as excellent background reading for anyone new to the field.
Considered a classic in the field, this book provides a comprehensive and rigorous treatment of statistical learning. It's suitable for those looking to deepen their understanding and is often used as a reference by researchers and practitioners. While more mathematically demanding than 'An Introduction to Statistical Learning,' it's invaluable for a thorough grasp of the theoretical underpinnings of advanced analytics techniques. It's a key reference for both academic study and professional work.
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 comprehensive overview of data visualization, covering the basics of visual perception, data types, and chart types. It great resource for beginners who want to learn the fundamentals of data visualization.
Offers a practical approach to developing predictive models, covering various techniques and considerations. It's well-suited for graduate students and working professionals who need to build and evaluate predictive models. It's a valuable reference for understanding the process of model building, from data preprocessing to model evaluation. This book is highly regarded for its practical examples and detailed explanations.
Dives into common mistakes and suboptimal solutions in database design and SQL coding, explaining how to identify and correct them. It's crucial for deepening understanding and writing more robust and maintainable SQL. It's a valuable resource for developers and database professionals seeking to improve their practices.
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.
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.
Offers an accessible and engaging introduction to the fundamentals of statistics, a critical component of data analytics. It explains key statistical concepts using real-world examples and relatable anecdotes, making it an excellent resource for those without a strong mathematical background. It helps build a solid foundation in statistical thinking necessary for data analysis.
Focuses on using SQL specifically for data analysis, covering techniques beyond basic querying to transform and gain insights from data. It's suitable for those who have a foundational understanding of SQL and want to apply it in a data analysis context. It's relevant for data scientists and analysts.
While focused on MySQL, this book provides in-depth coverage of database and SQL performance tuning that is applicable to other systems. It's essential for those working with large datasets and high-traffic applications. It's a comprehensive guide for optimizing SQL queries and database infrastructure.
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.
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.
In the age of information overload, this book provides essential tools and frameworks for critically evaluating data, statistics, and claims presented as data-driven. It equips readers with the skills to identify misleading information and understand the potential for manipulation. This highly relevant book for navigating the contemporary data landscape.
While focused on T-SQL (for Microsoft SQL Server), this book provides a deep and rigorous understanding of SQL fundamentals that are applicable to many dialects. It's highly regarded for its clear explanations and challenging examples, making it excellent for solidifying core concepts. It's often used by professionals working with SQL Server.
This handbook takes a pragmatic approach to dealing with messy, real-world data. It provides a collection of techniques and war stories for handling various data quality issues. It valuable resource for practitioners who encounter challenging data problems regularly and offers practical solutions and insights.
Tailored for aspiring data scientists, this book focuses on the SQL skills needed for data analysis, including dataset construction and manipulation. It bridges the gap between basic SQL and its application in data science workflows. Relevant for those entering or working in the data science field.
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.
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.
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.
Offers an approachable and practical introduction to SQL, particularly for those interested in data analysis and storytelling. It uses PostgreSQL for examples but the concepts are widely applicable. It's great for beginners and those who want to apply SQL skills to real-world data tasks.
Provides a hands-on approach to learning and practicing SQL through a variety of problems. It's an excellent supplement to theoretical learning and helps solidify understanding through practical application. Suitable for all levels looking to improve their problem-solving skills with SQL.
Offers a captivating and accessible introduction to predictive analytics, explaining how it works and its impact on everyday life. It's suitable for a broad audience, including those in high school and undergraduates, providing a good overview of the topic without excessive technical detail. It's more of an introductory read to spark interest and provide context rather than a deep technical reference. The book uses compelling case studies to illustrate the power of predictive modeling.
Provides a comprehensive introduction to predictive data analytics through the lens of machine learning. It balances theory with practical applications, making it suitable for undergraduates and graduate students. It's a good resource for gaining a solid understanding of various machine learning algorithms used in predictive analytics, with case studies to illustrate real-world usage. This book is helpful for both learning foundational concepts and seeing their practical relevance.
Provides practical tips and tools for data wrangling using Python. It is suitable for beginners and those who want to improve their efficiency in handling messy data with Python. It covers various techniques for cleaning, transforming, and reshaping data.
Comprehensive guide to using Tableau for visual analysis. It covers the core features of Tableau, including the creation and use of calculated fields to enhance visualizations and perform deeper analysis. It's a practical resource for anyone looking to master data visualization and analysis using Tableau.
A classic since its publication, this book remains highly relevant in highlighting how statistics can be manipulated or misinterpreted to present a misleading picture. It teaches readers to critically evaluate statistical claims and recognize common pitfalls. This valuable read for anyone who works with or consumes data and wants to avoid being misled.
Given the increasing importance of ethical considerations in data science, this book is highly relevant for all levels, particularly for those working with data in practice. It explores the ethical implications of data analytics through concepts and case studies, providing a crucial perspective often missing in technical texts. is essential for understanding the broader societal impact and responsibilities associated with advanced analytics. It recent publication addressing contemporary topics.
Focuses on performing advanced analytics using PySpark, Spark's Python API, which is crucial for handling large-scale data. It's relevant for graduate students and professionals working with big data platforms. It covers best practices in Spark programming and statistical methods for analytics on distributed data. This book is valuable for those dealing with big data challenges in advanced analytics.
A practical guide to data visualization using Tableau, emphasizing the principles and best practices of effective data storytelling.
Complementing the book by Martin, this resource delves deeper into the ethical considerations in data science with concepts, techniques, and cautionary tales. It's important for all levels, particularly for those developing and deploying analytical models. It provides frameworks and examples for navigating ethical challenges in the field. is crucial for responsible practice in advanced analytics.
Offers a comprehensive introduction to forecasting methods, covering both theoretical principles and practical applications. It uses R to demonstrate various forecasting techniques and is suitable for students and practitioners in various fields. It's a key resource for anyone involved in time series analysis and forecasting as part of their data analytics work.
Offers practical tips and techniques for writing more effective and efficient SQL code. It's a great resource for improving coding style and avoiding common pitfalls. Suitable for those with some SQL experience looking to refine their skills.
Classic in the field of reinforcement learning, another important area within advanced analytics, particularly for decision-making and automation. It's suitable for graduate students and researchers interested in this specific area. While the first edition classic, the second edition provides updated coverage. This book is valuable for understanding the principles of learning through interaction.
A deep dive into how the SQL Server Query Optimizer works, which is essential for advanced performance tuning. is highly technical and geared towards those who need to understand the low-level details of query execution. It's valuable for database administrators and senior developers.
Written by a renowned statistician and forecaster, this book explores the art and science of prediction in various fields using data and statistical modeling. It discusses the challenges of making accurate predictions in a world filled with uncertainty and noise. It's a thought-provoking read for anyone interested in predictive analytics and the limitations of data.

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