Understanding Bernoulli Distribution in Python (Numpy & Scipy)

🧠 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-automations-4579 In this video, you’ll learn how to work with the Bernoulli distribution using Python. Whether you're studying probability, building simulations, or working on binary classification problems, this tutorial will give you a strong foundation—with hands-on examples using NumPy and SciPy. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *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: https://www.youtube.com/playlist?list=PLcQVY5V2UY4LF-eHg0tfTpWHhgpX9XF4S Binomial Distribution: https://youtu.be/bZfnCdLLxAI Uniform Distribution: https://youtu.be/6hI_8rqHhD8 Python Law of Large Numbers: https://youtu.be/xKNq90VrM7k In this video, I break down the Bernoulli distribution in Python and show you exactly how to implement it using NumPy, SciPy, and Matplotlib. The Bernoulli distribution represents binary outcomes in a single trial, like a coin flip, a YouTube subscription decision, or a baseball pitch being a strike or ball. Unlike the binomial distribution which involves multiple trials, the Bernoulli focuses on just one trial with two possible outcomes. I walk through several practical examples including YouTube subscriber conversions and coin flips to help you understand the key concepts. We explore how to generate Bernoulli distributions using both NumPy and SciPy, calculate the probability mass function (PMF) to find exact probabilities, and work with cumulative distribution functions (CDF). You'll learn the critical difference between Bernoulli and binomial distributions, when to use each one, and how to visualize your results with histograms, bar charts, and step plots. By the end of this tutorial, you'll have a solid understanding of the Bernoulli distribution, know how to implement it in your own data science projects, and be able to calculate and visualize probability distributions with confidence. Perfect for anyone learning statistics, probability theory, or working on data science projects that involve binary classification problems. TIMESTAMPS 00:00 Introduction to Bernoulli Distribution 01:02 Bernoulli vs Binomial Distribution Concepts 02:36 Setting Up Python Environment & Imports 03:30 Example 1: Generating Bernoulli Distribution with NumPy 05:52 Example 2: Coin Flips in SciPy 07:22 Example 3: Calculating PMF (Probability Mass Function) 08:50 Example 4: Calculating CDF (Cumulative Distribution Function) 10:25 Plotting Histogram of Coin Flips 12:35 Example 6: PMF Coin Flip Visualization 15:16 Example 7: CDF Coin Flip Visualization 19:01 Summary and Next Steps OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/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.