Statistics and Mathematics for Data Science and Analytics

You do not need a formal mathematics degree to succeed in data science and analytics, but you do need a solid grasp of the core statistical concepts that drive data decisions. This course bridges the gap between complex mathematical theory and practical, real-world data application. Through clear written explanations and step-by-step examples, you will build a strong foundation in probability, hypothesis testing, and regression analysis. You will transition from simply running algorithms to truly understanding the mathematical logic behind your data insights and predictive models. What you'll learn: - Understand foundational probability theory and descriptive statistics to summarize data effectively. - Apply hypothesis testing and analysis of variance (ANOVA) to make data-driven business decisions. - Master linear and logistic regression models to identify trends and make accurate predictions. - Explore essential machine learning mathematics, including decision trees and basic optimization concepts. - Understand modern model evaluation metrics and basic Bayesian principles used in modern A/B testing. The curriculum begins with fundamental terminology and probability basics before guiding you through descriptive statistics, inferential methods, and regression models. You will read through practical scenarios that demonstrate how these mathematical concepts are applied to solve actual business problems. This course is designed for aspiring data scientists, business analysts, and beginners who want to build a strong mathematical foundation without getting lost in academic jargon. No advanced mathematical background is required. Start your journey toward mastering the mathematical foundations of data science today.