Statistical Modeling and Inference

О курсе

Learn the fundamentals of statistical modeling and inference through this comprehensive course in the Data Analytics Skill Path. You will develop critical competencies including applying probability concepts to calculate event likelihoods, identifying common probability distributions, describing distribution shapes, writing and executing statistical code, generating fundamental statistical visualizations, constructing confidence intervals, performing hypothesis testing, and building and interpreting simple linear regression models. Through hands-on practice with Python and relevant statistical libraries, you will explore datasets, quantify uncertainty, test hypotheses, and uncover predictive relationships between variables. This course combines expertise from Google and Packt, providing multiple perspectives on statistics and data analysis. You will progress from foundational probability and descriptive statistics to data visualization, confidence intervals, hypothesis testing, and predictive modeling with linear regression. The curriculum balances theoretical understanding with practical application, preparing you to confidently apply statistical methods to real-world data science problems. Perfect for aspiring data scientists and analysts seeking strong foundations in statistical reasoning, coding, and modeling.

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