Math and Statistics for Data Analysts: Beyond Excel and SQL

Many analysts can write SQL queries or build Excel sheets, but struggle to interpret what the numbers actually mean. To make reliable, data-driven decisions, you need a solid grasp of the mathematical and statistical principles that drive data analysis.\n\nThis text-based course bridges the gap between basic tool usage and advanced data reasoning. You will learn how to formulate hypotheses, analyze distributions, and perform statistical tests using Python, transforming raw numbers into actionable business insights.\n\nWhat you'll learn:\n- Understand core probability concepts and how they apply to real-world business scenarios.\n- Analyze and interpret key probability distributions to model data behavior accurately.\n- Calculate and explain correlation and covariance to identify relationships between variables.\n- Perform and interpret ANOVA (Analysis of Variance) tests to compare multiple datasets.\n- Apply statistical methods using modern Python libraries like pandas and scipy.stats.\n- Formulate and test hypotheses to make evidence-based decisions instead of relying on guesswork.\n\nYou will start with fundamental terminology and basic probability concepts before moving on to distribution models, correlation, and hypothesis testing. Each concept is explained in clear written detail alongside practical Python code snippets that show you how to apply these mathematical foundations immediately.\n\nThis course is designed for beginner data analysts, business analysts, and SQL or Excel users who want to build a strong mathematical foundation. No prior advanced math background is required, though a basic familiarity with Python is helpful.\n\nStart reading today to elevate your analytical skills and make decisions with mathematical confidence.