Agenda:

• 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.
• Fit a logistic regression model for predictive modeling.
• Explain the statistical foundations of machine learning.
• Discuss data difficulties and modeling issues and their statistical solutions.

Who Should Attend:

Anyone in the field of data science who does not yet have a deep understanding of statistical and machine learning concepts or wants to enhance their knowledge, which might include business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, mathematicians, and others who work in allied areas.

Prerequisites:

Before attending this course, you should have experience using computer software. It is beneficial if you have completed the equivalent of an undergraduate course in statistics covering distribution of data, p-values, hypothesis testing, and regression. No prior SAS experience is needed.