Tableau Desktop Specialist - Essentials
This free, self-paced data literacy course series is the first step to becoming a data analyst. Learn how to explore, understand, and communicate insights through data visualizations. This course series covers key topics, including data collection and structure, field and variable types, statistical thinking, correlation and regression, and visualizing data. At the end of this series, you will be ready to take the next step toward your career goal and register for MicroTrain's 3-day live, instructor-led online Tableau Desktop Specialist Certification training.
Outline
Learning Objectives
At the end of this course series, you will be able to:
- Define data literacy.
- Describe the importance of asking "good" questions.
- Identify aptitudes and attitudes for working effectively with data.
- Describe what data is, how it's collected, and where it's stored.
- Identify different types of variables.
- Distinguish among nominal qualitative, ordinal qualitative, and quantitative variables.
- Distinguish between discrete and continuous variables.
- Distinguish among different types of aggregations.
- Identify how aggregation and granularity impact data.
- Distinguish between frequency and proportion distributions for discrete variables.
- Identify shapes of distributions for continuous variables.
- Demonstrate an understanding of histograms and box plots.
- Calculate mean, variance, and standard deviation.
- Demonstrate an understanding of continuous distributions.
- Define the use and limitations of p-values in hypothesis testing.
- Distinguish between strong and weak correlations.
- Differentiate between characteristics of correlation and linear regression.
- Identify characteristics of meaningful data.
- Differentiate between well-structured and poorly formatted data.
- Identify data restructuring options.
- Define data literacy.
- Describe the importance of asking "good" questions.
- Identify aptitudes and attitudes for working effectively with data.
- Describe what data is, how it's collected, and where it's stored.
- Identify characteristics of meaningful data.
- Differentiate between well-structured and poorly formatted data.
- Identify data restructuring options.
Prerequisites
None
Exam Pass Guarantee
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Student Employment Success - Project/Process Manager
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PMP Certification Training, 2008
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