Statistics with Python: Poisson Distribution Fundamentals

Understanding how to model the frequency of random events is a vital skill in data analysis and statistics. This text-based course introduces you to the Poisson distribution and shows you how to apply it practically using clean, modern Python code. You will gain a solid understanding of discrete probability distributions and learn how to use Python libraries like NumPy and SciPy to calculate probabilities, simulate event arrivals, and analyze real-world count data. What you'll learn: - Understand the mathematical foundation and core assumptions of the Poisson distribution - Calculate probability mass functions and cumulative distribution functions using Python - Model real-world event frequencies such as website visits, customer arrivals, and error rates - Apply modern Python libraries including SciPy and NumPy for statistical simulations - Evaluate data distributions using summary statistics, expectation, and variance - Practice interpreting statistical outputs to make informed data-driven decisions The course begins with fundamental terminology and basic probability concepts before moving step-by-step through practical coding exercises and applied modeling scenarios. This course is designed for beginners in statistics and Python programming who want to expand their data science toolkit. Start reading today to build practical statistical modeling skills with Python.