Tableau 10 for Data Scientists
Tableau 10 provides data scientists with the tools to easily format, filter, analyse, and visualise data. With its powerful features, users can quickly create maps and dashboards to gain insights from their data. ▼
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Course Feature
Cost:
Free Trial
Provider:
LinkedIn Learning
Certificate:
No Information
Language:
English
Course Overview
❗The content presented here is sourced directly from LinkedIn Learning platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
(Please note the following content is from the official provider.)
Learn how to deal with illegible or badly formatted data, how to use Tableau to answer key data analytics questions, and how to visualise your findings with maps and dashboards.
demonstrate how to use parameters to enhance visualisations, how to create cross-source filters, how to use data extracts to optimise slow connections, and much more.
Discover how actions can connect sheets and increase interactivity and performance, as well as how formatting can make an ordinary dashboard stand out.
Get some extra help with date and time calculations in Tableau.
This course delves deeply into the practical, applicable, and essential skills required of anyone working in data visualisation and analytics in a professional setting.
(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
Tableau 10 for Data Scientists is a course designed to help learners understand how to deal with illegible or badly formatted data, how to use Tableau to answer key data analytics questions, and how to visualise their findings with maps and dashboards. Learners will be able to demonstrate how to use parameters to enhance visualisations, how to create cross-source filters, how to use data extracts to optimise slow connections, and much more. Additionally, learners will discover how actions can connect sheets and increase interactivity and performance, as well as how formatting can make an ordinary dashboard stand out. Finally, learners will get some extra help with date and time calculations in Tableau. This course delves deeply into the practical, applicable, and essential skills required of anyone working in data visualisation and analytics in a professional setting.
[Applications]
After completing this course, participants will be able to apply their knowledge of Tableau 10 to their data science projects. They will be able to use Tableau to clean and format data, create visualisations, and build interactive dashboards. Participants will also be able to use parameters, filters, and data extracts to optimise their visualisations. Additionally, they will be able to use actions to connect sheets and increase interactivity and performance, as well as use formatting to make their dashboards stand out. Finally, participants will be able to use date and time calculations in Tableau.
[Career Paths]
1. Data Scientist: Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use a variety of tools and techniques, such as Tableau, to visualize data and create reports. Data Scientists are in high demand as organizations increasingly rely on data-driven decisions.
2. Business Intelligence Analyst: Business Intelligence Analysts use data to identify opportunities and develop strategies for businesses. They use Tableau to create visualizations and dashboards to help organizations make better decisions. They also use Tableau to analyze customer behavior and develop marketing strategies.
3. Data Visualization Designer: Data Visualization Designers use Tableau to create visually appealing and informative data visualizations. They use their design skills to create interactive dashboards and reports that help organizations make better decisions.
4. Data Engineer: Data Engineers use Tableau to build data pipelines and ETL processes. They are responsible for designing and developing data architectures and ensuring that data is stored and accessed efficiently. They also use Tableau to create data visualizations and dashboards.
[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the development of data-driven solutions to solve complex problems. It combines elements of computer science, mathematics, and statistics to provide students with the skills to analyze and interpret data. Students will learn how to use data to make decisions, develop predictive models, and create visualizations. Additionally, they will gain an understanding of the ethical implications of data science and the importance of data privacy.
2. Master of Science in Business Analytics: This degree path focuses on the application of data-driven solutions to business problems. Students will learn how to use data to identify trends, develop strategies, and optimize operations. They will also gain an understanding of the ethical implications of data science and the importance of data privacy. Additionally, they will learn how to use data to make decisions, develop predictive models, and create visualizations.
3. Master of Science in Data Science: This degree path focuses on the development of data-driven solutions to solve complex problems. It combines elements of computer science, mathematics, and statistics to provide students with the skills to analyze and interpret data. Students will learn how to use data to make decisions, develop predictive models, and create visualizations. Additionally, they will gain an understanding of the ethical implications of data science and the importance of data privacy.
4. Doctor of Philosophy in Data Science: This degree path focuses on the development of advanced data-driven solutions to solve complex problems. It combines elements of computer science, mathematics, and statistics to provide students with the skills to analyze and interpret data. Students will learn how to use data to make decisions, develop predictive models, and create visualizations. Additionally, they will gain an understanding of the ethical implications of data science and the importance of data privacy. They will also learn how to develop new algorithms and techniques to solve data-related problems.
The development trends for these degree paths are increasing demand for data-driven solutions, the need for data privacy, and the use of advanced algorithms and techniques to solve data-related problems. As the world becomes increasingly data-driven, these degree paths will become increasingly important for those looking to pursue a career in data science.
Course Syllabus
Discrete versus continuous
Rows and columns
Filters
Colors
Dates
Course Provider
Provider LinkedIn Learning's Stats at AZClass
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