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Updated in [March 06th, 2023]

This course provides an overview of Tableau for beginners. It covers topics such as connecting to a variety of data sources and cleaning up the information, combining several datasets (Union, Blending, cross-database Joins, and many more), data visualization in the form of charts, graphs, maps, plots, and so on, tips and tricks for using statistics to tell a clear and effective tale, Tableau Database or Fast Engine is a concept (Extracts), developing or changing your career path in the areas of data science and analytics, drilldown, sorting, grouping, and the development of sets and clusters, parameters, tooltips, annotations, and reference lines, creating a dashboard out of your report, using maps and playing with geographical data types, and calculations such as aggregate calculations, date, logical, string, number, and other sorts of calculations.

[Applications]
After completing this course, learners can apply their knowledge of Tableau to create visualizations from data sources, combine multiple datasets, and develop dashboards. They can also use Tableau to create maps and play with geographical data types, as well as use calculations such as aggregate calculations, date, logical, string, and number calculations. Additionally, learners can use Tableau to filter data using parameters, tooltips, annotations, and reference lines. Finally, learners can use Tableau to tell a clear and effective tale using statistics.

[Career Paths]
1. Data Scientist: Data Scientists are responsible for analyzing large amounts of data and developing insights from it. They use a variety of tools and techniques to uncover patterns and trends in data. They also develop predictive models and algorithms to help organizations make better decisions. The demand for Data Scientists is growing rapidly as organizations are increasingly relying on data-driven decisions.

2. Business Intelligence Analyst: Business Intelligence Analysts are responsible for collecting, analyzing, and interpreting data to help organizations make better decisions. They use a variety of tools and techniques to uncover patterns and trends in data. They also develop reports and dashboards to help organizations visualize their data. The demand for Business Intelligence Analysts is growing rapidly as organizations are increasingly relying on data-driven decisions.

3. Data Visualization Specialist: Data Visualization Specialists are responsible for creating visual representations of data to help organizations make better decisions. They use a variety of tools and techniques to create interactive visualizations that can be used to explore and analyze data. The demand for Data Visualization Specialists is growing rapidly as organizations are increasingly relying on data-driven decisions.

4. Tableau Developer: Tableau Developers are responsible for developing and maintaining Tableau dashboards and reports. They use a variety of tools and techniques to create interactive visualizations that can be used to explore and analyze data. The demand for Tableau Developers is growing rapidly as organizations are increasingly relying on data-driven decisions.

[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the fundamentals of data science, including data analysis, machine learning, and data visualization. It also covers topics such as data mining, predictive analytics, and artificial intelligence. The degree is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

2. Master of Science in Business Analytics: This degree path focuses on the application of data science to business problems. It covers topics such as data mining, predictive analytics, and artificial intelligence. It also covers topics such as data visualization, data engineering, and data governance. This degree is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

3. Master of Science in Data Science: This degree path focuses on the fundamentals of data science, including data analysis, machine learning, and data visualization. It also covers topics such as data mining, predictive analytics, and artificial intelligence. This degree is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

4. Doctor of Philosophy in Data Science: This degree path focuses on the advanced topics of data science, including data analysis, machine learning, and data visualization. It also covers topics such as data mining, predictive analytics, and artificial intelligence. This degree is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.