Statistics for Data Science and Analytics

Data science is more than writing code; it is about making sense of information through rigorous analysis. This text-based course bridges the gap between raw data and actionable insights by teaching you the core statistical principles that power modern data science. You will start with foundational terminology and descriptive statistics before moving on to advanced probability and inference techniques. By completing this course, you will transform how you view data, gaining the ability to validate assumptions, run experiments, and interpret complex data patterns with mathematical confidence. You will learn to think like a statistician and apply these methods to real-world datasets. What you'll learn: - Understand foundational statistical concepts, including variables, levels of measurement, and data types - Calculate and interpret measures of central tendency, dispersion, and shape in datasets - Apply probability distributions to model real-world scenarios and predict outcomes - Design and execute hypothesis tests, including t-tests, ANOVA, and chi-square tests, to make data-backed decisions - Utilize sampling techniques and confidence intervals to estimate population parameters accurately - Analyze relationships between variables using correlation and linear regression models - Explore modern data science practices, including basic A/B testing frameworks and error metrics This course is structured to build your knowledge from the ground up, starting with core definitions and descriptive statistics, then progressing to probability theory, and concluding with inferential statistics and regression modeling. Each concept is reinforced with clear written examples and conceptual exercises. This course is designed for beginners, aspiring data scientists, and business analysts who want a strong mathematical foundation. No prior background in advanced mathematics or programming is required. Start reading today to build a solid mathematical foundation for your data science journey.