Top Data Science YouTube Channels in 2026 from United Kingdom
Data Science is one of the most in-demand fields in technology, bridging the gap between raw data and actionable insights. Channels in this category cover the full spectrum of the discipline, from fundamental statistical analysis and data visualization to advanced machine learning and deep learning models. Whether you are aspiring to be a Data Scientist, Data Analyst, or Data Engineer, these creators provide the roadmap you need.
Content typically ranges from beginner-friendly tutorials on Python, R, and SQL to deep dives into libraries like pandas, NumPy, Scikit-learn, and TensorFlow. You'll also find extensive coverage of big data tools like Apache Spark and Hadoop, as well as business intelligence platforms like Tableau and PowerBI. Beyond syntax and tools, these channels often explore critical concepts such as data ethics, experimental design, and the mathematical foundations of algorithms.
Real-world applications are a major focus, with creators demonstrating how to clean messy datasets, build predictive models, and deploy AI solutions into production. Many channels also offer career advice, portfolio reviews, and mock interviews to help you navigate the competitive job market and land your first role in the data industry.
Channels
Egor Howell
@egorhowell
đ FREE Resume TemplateContact: egor@egorhowell.com
Egor Howellâs channel is a careerâfocused education hub for aspiring machineâlearning engineers and data scientists. It blends practical project tutorials, stepâbyâstep learning roadmaps, and free, downloadable resume templates designed to help viewers break into highâpay tech roles. Videos cover everything from foundational math and system design to realâworld ML project implementation and jobâsearch strategy. Egor often shares personal experiences, such as landing a $100K+ offer without a CS degree, giving authentic insight. The channel also promotes curated course links and 1âonâ1 coaching, adding extra value for serious career changers.
Egor combines free, highâquality resume templates with inâdepth, projectâdriven ML tutorials and candid career stories, offering a oneâstop resource for both skill acquisition and jobâsearch successâespecially valuable for those without a traditional CS background.
Aspiring ML engineers, data scientists, recent graduates, careerâswitchers, and techâsavvy professionals (typically ages 18â35) who want practical guidance and free resources to land dataâfocused roles.
Frequently Asked Questions
What is the best way to start learning Data Science?
A combination of theoretical study and practical application is best. Start by learning a programming language like Python or R, and the basics of statistics. Then, apply what you learn by working on projects, such as analyzing public datasets from Kaggle. Supplement this with online courses or tutorials from the channels listed here.
Should I learn Python or R for Data Science?
Both are excellent choices. Python is generally recommended for beginners due to its simple syntax and versatility, especially if you want to move into Machine Learning or Deep Learning. R is a powerhouse for statistical analysis and academic research. Many professionals end up learning the basics of both, but Python is currently more dominant in the industry.
Do I need a strong math background to become a Data Scientist?
While a degree in mathematics isn't strictly necessary, a solid understanding of certain mathematical concepts is crucial. Focus on Probability, Statistics, Linear Algebra, and Calculus (specifically derivatives and gradients for optimization). You don't need to be a mathematician, but you need to understand how algorithms work under the hood.
What kind of projects should I include in my portfolio?
Aim for a diverse mix of projects that showcase different skills: an Exploratory Data Analysis (EDA) project to show your storytelling ability, a Machine Learning project to demonstrate your modeling skills, and perhaps a deployed app (using Streamlit or Flask) to show you can put models into production.
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