Statistics for Machine Learning (Statistics You Need to Know for Machine Learning)
• Explain the relevance of statistics in big data and machine learning.
• Relate statistical and data science terminology.
• Generate descriptive statistics and explore data with graphs.
• Detect associations among variables.
• Perform linear regression for explanatory modeling.
• Compare explanatory modeling with predictive modeling.
• Describe the trade-off between bias and variance.
• Fit a logistic regression model for predictive modeling.
• Score new data.
• Explain the statistical foundations of machine learning.
• Discuss data difficulties and modeling issues and their statistical solutions.
Installation of Power BI Desktop
• System requirements for Power BI Desktop
• Step-by-step installation guide
• Initial setup and configuration
• Updating Power BI Desktop
• Troubleshooting common installation issues
• Licensing and subscription options
• Importing sample datasets for practice
Connecting Power BI with Other Sources
• Supported data sources in Power BI
• Connecting to databases, cloud services, and files
• Data import methods and best practices
• Managing data connections
• Data refresh and scheduling
• Handling data privacy and security
• Using Power BI gateways
Predictive Modeling using Visual Statistics (SAS Visual Statistics in SAS Viya: Interactive Model Building)
• Perform statistical analysis of data of any size.
• Create a report with pages.
• Determine useful preferences and settings.
• Create segments, or clusters, of input variables.
• Perform regression and logistic regression modeling.
• Add splines to models.
• Perform decision tree modeling.
• Perform stratified model fitting.
• Perform model validation.
• Compare models.
• Generate score code.
Decision Automation using SAS Intelligent Decisioning (SAS Intelligent Decisioning: Essentials)
• Create and manage decisions and their objects in SAS Intelligent Decisioning.
• Understand the foundation needed for advanced courses based on SAS Intelligent Decisioning.
Responsible Innovation and Trustworthy AI
• Explain how trustworthy AI integrates with the AI and analytics life cycle and the data supply chain.
• Identify unwanted biases throughout the AI and analytics life cycle.
• Define and apply principles of responsible innovation (human-centricity, inclusivity, accountability, privacy and security, robustness, transparency).
• Identify how SAS technologies address bias and innovate responsibly.
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