Tableau Desktop Specialist + Data Analyst Certification

Overview

Do you want to enhance your skills and learn everything required to pass both the Desktop Specialist and Data Analyst exam?

Syllabus

• Introduction to Tableau
Overview of Tableau Desktop
Key functionalities and user interface
Installing Tableau and setup instructions
• Connecting to Data
Understanding data sources and establishing connections
Working with different file types and databases
Data extraction and live connections
• Preparing Data
Data cleaning and transformation techniques
Using data interpreter and pivoting data
Creating data extracts and filters
• Creating Visualizations
Building basic charts: bar charts, line graphs, scatter plots
Advanced visualizations: maps, dual-axis charts, and histograms
Creating dashboards and stories
• Analyzing Data
Applying filters and sets
Using calculated fields and table calculations
Trend lines, forecasting, and clustering
• Enhancing Analysis
Using parameters and calculated fields for dynamic analysis
Advanced analytics features: LOD expressions
Understanding reference lines, bands, and distribution models
• Sharing and Collaborating
Publishing data to Tableau Server or Tableau Online
Exporting to PDF, PowerPoint, and image formats
Sharing visualizations on Tableau Public
• Exam Preparation: Tableau Desktop Specialist
Overview of the exam format and topics
Practice questions and review sessions
Test-taking strategies and tips
• Practical Case Studies
Hands-on exercises with real-world data
End-to-end project: from data connection to dashboard creation
• Introduction to Data Analysis
Key concepts in data analysis and statistics
Exploratory data analysis techniques
• Preparing for Data Analyst Certification
Overview of the exam and skill requirements
In-depth analysis practice tasks
Mock exams and revision sessions
• Course Conclusion
Review of key concepts and skills
Next steps in further learning and certification paths
Resources for continuous learning and support
• Final Assessment
Comprehensive project or exam encompassing course material

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Taught by

Nikolai Schuler and Ben Butters

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Subjects

Data Science