Binomial Distribution in Python: A Beginner's Guide with Scipy & Numpy скачать в хорошем качестве

Binomial Distribution in Python: A Beginner's Guide with Scipy & Numpy

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... New to probability or statistics in Python? In this step-by-step tutorial, you'll learn how to work with the Binomial Distribution using SciPy and NumPy—perfect for beginners exploring probability, experiments, or real-world scenarios like coin flips and success/failure events! Code: https://ryanandmattdatascience.com/bi... 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT Statistics for Data Science Playlist:    • Statistics for Data Science   Python Normal Distribution:    • Normal Distribution in Python: A Beginner'...   Python Law of Large Numbers:    • Understanding the Law of Large Numbers in ...   Python Bernoulli Distribution:    • Understanding Bernoulli Distribution in Py...   In this video, I walk you through the binomial distribution using Python and show you seven practical examples that bring this important statistical concept to life. We explore the binomial distribution by working with real-world scenarios like YouTube subscriber conversion rates, coin flips, and baseball pitches. You'll learn the key difference between binomial and Bernoulli distributions, understand when to use each one, and see how to implement them using popular Python libraries. I demonstrate how to create binomial distributions using both NumPy and SciPy, calculate exact probabilities with the PMF (probability mass function), and find cumulative probabilities using the CDF (cumulative distribution function). Beyond just the math, I show you how to visualize these distributions through three different types of graphs: histograms, stem plots for PMF, and line plots for CDF using Matplotlib and Seaborn. By the end of this tutorial, you'll understand what makes a distribution binomial, how to model success/failure scenarios, and most importantly, how to apply these concepts in Python to solve real problems. Whether you're analyzing conversion rates, modeling random events, or just learning statistics, this video gives you hands-on experience with one of the most fundamental probability distributions in data science. TIMESTAMPS 00:00 Introduction to Binomial Distribution 01:00 What is Binomial Distribution? 02:10 Binomial vs Bernoulli & Key Concepts 02:50 Importing Python Libraries 03:50 Example 1: YouTube Subscribers (NumPy) 05:17 Example 2: Coin Flips (SciPy) 07:32 Example 3: PMF (Probability Mass Function) 08:46 Example 4: CDF (Cumulative Distribution Function) 10:02 Example 5: Plotting a Histogram 11:22 Example 6: Plotting PMF 13:53 Example 7: Plotting CDF 16:02 Recap & Conclusion OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.