10 YouTube Channels Every Aspiring Data Scientist Should Follow in 2025

10 YouTube Channels Every Aspiring Data Scientist Should Follow in 2025

This article highlights ten essential YouTube channels that aspiring data scientists should follow to build crucial skills for 2025. The digital landscape offers a wealth of free learning resources, and YouTube stands out as a prime platform for acquiring knowledge in data science, machine learning, AI, and related fields. These channels provide simple videos, practical tips, real project ideas, and clear explanations of complex topics.

Key YouTube Channels for Data Science Learning:

1. StatQuest with Josh Starmer:

   • Focus: Simplifies complex topics in statistics, machine learning, data science, and AI.
   • Content: Engaging visuals and step-by-step explanations of concepts like Support Vector Machines and Random Forests.
   • Subscribers: Over 1 million.
   • Value: Excellent for understanding data science fundamentals.

2. Krish Naik:

   • Focus: Machine learning, deep learning, and artificial intelligence with practical, hands-on tutorials.
   • Content: Real-world application of data science techniques. Popular videos include "Complete Statistics for Data Science in 6 Hours" and "Complete Machine Learning in 6 Hours."
   • Subscribers: Over 1 million.
   • Value: Great for both beginners and experienced professionals.

3. Corey Schafer:

   • Focus: Software development and programming, with a strong emphasis on Python.
   • Content: Clear tutorials on Python, Git, SQL, JavaScript, terminal commands, and computer science basics. The "Python Tutorials" series is highly recommended.
   • Subscribers: Over 1 million.
   • Value: Essential for building strong programming foundations.

4. Alex The Analyst:

   • Focus: Preparing individuals for a career as a Data Analyst.
   • Content: Beginner-friendly content on Python, SQL, and career advice. The Data Analyst Bootcamp playlist is particularly useful.
   • Subscribers: Over 1 million.
   • Value: Ideal for those entering the data analysis field.

5. codebasics:

   • Focus: Data analytics, data science, and AI through practical tutorials and projects.
   • Content: Hands-on projects and career advice. The "Data Analysis Projects" playlist helps apply skills to real-world scenarios.
   • Subscribers: Not specified, but implied to be substantial.
   • Value: Focuses on practical skills for data jobs.

6. Ken Jee:

   • Focus: Data science projects, career advice, and practical tips.
   • Content: Features "Kaggle Projects" walkthroughs. Also hosts a podcast, "Ken's Nearest Neighbors."
   • Subscribers: Over 250,000.
   • Value: Helpful for career advancement and project-based learning.

7. Data School:

   • Focus: Helping beginners learn data science and prepare for jobs.
   • Content: Easy-to-understand tutorials on essential data science topics. The "Data Analysis in Python" playlist is a highlight.
   • Subscribers: Over 250,000.
   • Value: Makes complex data science concepts accessible.

8. Data Professor:

   • Focus: Data science, machine learning, and bioinformatics with accessible tutorials.
   • Content: Explainer videos and hands-on tutorials, including a "Python Data Science Project" playlist.
   • Subscribers: Over 250,000.
   • Value: Great for learning practical data science applications.

9. The PyCoach:

   • Focus: Learning Python through tutorials and practical coding advice.
   • Content: Real-world projects, including a popular "Python Web Scraping Tutorials" playlist.
   • Subscribers: Over 40,000.
   • Value: Builds hands-on coding skills for beginners.

10. Egor Howell:

    • Focus: Data science and machine learning career advice.
    • Content: Insights into breaking into the field, job tips, and personal experiences. The "Data Science Career Advice" playlist covers interviews and resumes.
    • Subscribers: Over 20,000.
    • Value: Essential guidance for career navigation in data science.

Conclusion:

These YouTube channels offer comprehensive resources for anyone looking to learn data science. They cover essential topics like coding, statistics, and machine learning in an accessible manner, helping viewers enhance their skills and prepare for successful careers in the field. The article encourages continuous learning and practice.

Author Information:

Jayita Gulati is a machine learning enthusiast and technical writer with a Master's degree in Computer Science from the University of Liverpool. She is passionate about building machine learning models.