Understanding Discrete and Continuous Variables in Python for Data Analysis скачать в хорошем качестве

Understanding Discrete and Continuous Variables in Python for Data Analysis

🧠 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... Confused about the difference between discrete and continuous variables? In this tutorial, you'll learn how to identify, classify, and work with both types of variables in Python—a key skill for anyone doing data analysis, statistics, or machine learning. 🚀 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   Binomial Distribution:    • Binomial Distribution in Python: A Beginne...   Python Right & Left Skewed Data:    • Python Skewness Explained: Right, Left & S...   Normal Distribution:    • Normal Distribution in Python: A Beginner'...   In this video, I break down the fundamental difference between discrete and continuous variables in statistics and show you exactly how to work with them in Python. Understanding discrete vs continuous data is crucial for data science because it impacts which statistical distributions you use and how you visualize your data. We start by clearly defining what makes a variable discrete (countable, distinct values like integers) versus continuous (measurable, any value within a range). I walk through real-world examples like dice rolls, race participants, heights, and running distances to help you identify each type in practice. Then we jump into Python code using NumPy, Matplotlib, and Seaborn to generate and visualize both types of variables. I demonstrate how to create discrete data with a uniform distribution using dice rolls, and continuous data with a normal distribution using heights. You'll see side-by-side histogram visualizations that make the difference crystal clear. By the end of this video, you'll confidently recognize discrete and continuous variables in your own data science projects and know exactly how to handle each type in Python. This foundation is essential as we continue building out this statistics series with more advanced topics. If you're working through statistics for data science or preparing for data analysis work, this tutorial will give you the clarity you need to move forward with confidence. TIMESTAMPS 00:00 Introduction to Discrete vs Continuous Variables 00:38 Discrete Variables Explained 01:46 Continuous Variables Explained 03:26 Setting Up Python Libraries 04:03 Example 1: Discrete Data (Dice Rolls) 05:14 Example 2: Continuous Data (Heights) 06:42 Plotting Discrete and Continuous Data 10:10 Analyzing the Results 10:54 Key Takeaways and Recap 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.