Decision Science

 Understanding probability distributions is essential for anyone working in data science , statistics , or machine learning . In this blog, we’ll break down three of the most common distributions  —  Normal , Binomial , and Poisson  — along with easy-to-follow Python examples using real-world data. Whether you’re building a predictive model or analyzing data patterns, mastering these distributions will sharpen your skills. Let’s dive in! What Are Probability Distributions? A probability distribution describes how the values of a random variable are distributed. It tells you the probability of different outcomes — kind of like a weather report, but for data! There are two broad types: Discrete distributions : Deal with countable outcomes (e.g., number of cars). Continuous distributions : Deal with outcomes that can take any value within a range (e.g., height, weight). 1. Normal Distribution — The Bell Curve Superstar What is it? The Normal distribution is a continuous distri...

Today, we’re diving deep into the Titanic dataset  — yes, the one where Jack could’ve probably fit on that door.  Our mission? To examine distribution, skewness, centering, and other properties of the dataset. No fluff — just straightforward Python code and simple explanations with a touch of humor. Let’s set sail! 1. Loading the Dataset: Meet the Titanic Passengers First, let’s import our tools and load the dataset. import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import scipy.stats as stats # Load Titanic dataset data = sns.load_dataset( 'titanic' ) data.head() Explanation:  We’re using three key libraries: Pandas: For data manipulation Seaborn: For visualization (and the Titanic dataset) Matplotlib: For displaying plots Output:  The first five rows of the dataset, featuring columns like survived , pclass , sex , age , and fare . 2. Visualizing Data Distributions Let’s start by visualizing the age distribution — because age played ...