Chi-square Test
Key Techniques for Data Interpretation
How to Create Machine Learning Prototypes
Creating Visual Reports that Inform Strategy
Outputs
Techniques for Effective Predictive Modeling
Creating Interactive Dashboards
How to Create a Machine Learning Dashboard
Quality Assurance 
Model Validation
Testing predictive models to ensure they perform as expected
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Key Techniques for Data Interpretation Key concepts include: Hypothesis
Testing: A method of making decisions using data, whether to accept or reject a hypothesis
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How to Create Machine Learning Prototypes Collect Data Gather relevant data that can be used to train and
test the machine learning model
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Creating Visual Reports that Inform Strategy Test and Iterate Before finalizing a report, test it with a sample audience to gather feedback
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Outputs Test Outputs Regularly: Regularly test outputs to ensure they are functioning correctly and delivering the desired sound quality
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Techniques for Effective Predictive Modeling Common validation techniques include: Train-
Test Split: Dividing the dataset into training and testing subsets to evaluate model performance
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Creating Interactive Dashboards Test and Validate: Before deployment, test the dashboard with real users to gather feedback and make necessary adjustments
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How to Create a Machine Learning Dashboard Test and Iterate After creating the dashboard, it is important to test it with actual users
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Statistical Techniques for Effective Marketing Key techniques include: Hypothesis
Testing: A method to test assumptions about a population parameter
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Visual Strategies for Analyzing Performance Test for Clarity: Before finalizing visualizations, test them with a sample audience to ensure clarity and effectiveness
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