Best Data Science Videos on YouTube in 2024

The world of data science is rapidly changing. For data scientists, this means continuous learning and exploration. YouTube is a goldmine of high-quality educational content on any topic.

Therefore, the following guide presents a comprehensive overview of the best Data Science YouTube channels and videos in 2024, aimed at all levels of Data Science learners, from complete beginners to seasoned professionals.

Just starting your data science journey? Here are some of the best data science YouTube channels to help you level up your data science skills in 2024.

3 blue 1 brown: Grant Sanderson makes great use of animations on his channel. He perfectly explains very difficult mathematics, linear algebra, calculus, and neural networks. Even if you have never been exposed to these topics before, you will have a lot of fun learning through 3Blue1Brown's interactive content.

Josh Sterner's StatQuest: Through his channel, Josh Starmer explains all manner of statistical concepts in a highly engaging and enlightening way. From hypothesis testing to p-values, he provides easy-to-understand explanations and fun visuals to make statistical fundamentals stick deep in the mind of a data scientist.

freeCodeCamp.org: This is a non-profit online organization that offers end-to-end courses on Python programming. This language is crucial in data science. Structured videos with hands-on exercises teach you the right programming skills to effectively manipulate and analyze data.

Datacamp: DataCamp offers introductory videos covering various concepts in data science such as data wrangling, visualization, machine learning, etc. The beginner tutorials are a good starting point to get an overview of the core features of data science.

Data Science Dojo: This channel covers just what you need to get started in data science. Content includes data types, the pandas and NumPy libraries, basic techniques for data analysis, and more.

Now that you've learned the basics, it's time to dive into different areas of data science. Here are some great channels to help you further develop your skills:

Ken G: This is Ken Jee's channel that focuses on practical data science applications. He provides project-based tutorials on machine learning algorithms such as natural language processing and deep learning, teaching various real-world insights.

Krish Naik: Krish Naik's channel is similar to Ken Jee's and focuses on project-oriented learning. He walks you through building a data science project from scratch and teaches problem-solving skills using best practices.

Andreas Kretz: For those who prefer a more theoretical approach, Andreas Kretz's channel provides in-depth explanations of machine learning algorithms and data engineering. He dives deep into the mathematical underpinnings of data science methods, thereby giving them a solid theoretical foundation.

Simple Learning: This channel has structured courses on various specializations in data science, such as data visualization, business analytics, etc. Comprehensive video lectures help learners gain a general understanding of a particular area in the field of data science.

Siraj Raval: He is an Artificial Intelligence and Deep Learning educator who covers a variety of topics, explaining most of the difficult concepts related to AI in very clear terms and conveying them to a larger audience through examples.

As you advance further in the field of data science, the need to stay up to date on the latest trends and advancements increases. Here are some great channels to help you stay differentiated:

2 Minute Paper: This channel provides “light” summaries of the latest research papers in most areas of AI and Machine Learning. This is the perfect channel to stay up to date with cutting edge research and focus on new topics in the field of Data Science.

Sendex: This channel by Sebastian Trautsch covers all the advanced deep learning techniques and frameworks like TensorFlow, PyTorch, etc. in great detail. His tutorials and explanations are also more advanced for experienced learners who want to explore the depths of deep learning further.

Corey Schafer: If you're interested in data engineering and creating robust data pipelines, Corey Schafer's channel is the perfect choice. He covers topics like Apache Spark, Apache Airflow, and cloud platforms like AWS, and provides practical guidance on building scalable data infrastructure.

JOMATEC: It provides an in-depth look at various machine learning algorithms and their applications to computer vision and natural language processing, which will be extremely useful for advanced learners who want to focus on these areas of data science.

CS Dojo: This is Michael Nielsen's channel that delves deep into the theoretical aspects of computer science, especially machine learning, and focuses on complex topics related to information theory, reinforcement learning, and deep learning architectures, catering specifically to those with a strong mathematics background.

YouTube is a rich repository of data science resources, but it's important to remember that it's only part of the picture. Here are some other suggestions to extend your learning:

Online Courses: Coursera, edX, and Udacity are the flag-bearers for online courses, with a comprehensive data science course inventory offered by leading Ivy League universities and industry thought leaders. These courses offer structured learning pathways with assessed assignments and projects.

Books and articles: Many of the best books on data science provide insight into the theoretical side of things. Some of them are books by Tom Mitchell, Andreas Müller, and Gareth James. These books will help you learn most of the theoretical elements of data science with practical advice. Keep yourself updated with the latest research by reading data science related blogs and articles posted in reputable publications.

Kaggle Competition: Kaggle is a great platform for various data science competitions and this course will help you apply those skills to case studies. Participating in these competitions will allow researchers to collaborate with other top data scientists, hone their problem-solving skills and build a strong portfolio.

Contributing to open source projects: Contributing to open source projects on data science libraries and frameworks is a great way to gain practical experience, interact with the data science community, and deepen your knowledge of the tools being used.

The life of a data scientist is a continuous journey of learning and discovery. YouTube provides a platform where you can access quality educational videos on various topics related to data science. If you stick to watching these channels and videos until the end, you can learn a lot and become a better data science professional.

Remember, your learning doesn't end with watching videos. You should utilize the other resources mentioned above to understand and stay up to date with the ever-changing field of data science. There are a variety of data science courses that can potentially guarantee you a job.

Not a problem: YouTube is packed with resources for those just getting started. Channels like 3Blue1Brown and StatQuest make complex math concepts like linear algebra and statistics fun and easy to understand, while freeCodeCamp.org and DataCamp offer introductory tutorials on Python programming and key data science functions.

Several channels focus on practical data science applications: Ken Jee and Krish Naik provide detailed project-based tutorials that show you how to build machine learning models and data science projects from scratch, helping you gain problem-solving skills and best practices that can be useful in real-world scenarios.

There are also more theoretical foundations on YouTube: Andreas Kretz is a channel that dives deep into different methods of data science and the mathematics behind algorithms used in machine learning and data engineering. Simplilearn's courses are well-structured and allow you to gain deeper knowledge of specific subdomains of data science.

In this fast-changing field, you need to keep up with the latest developments. Channels such as Two Minute Papers provide you with summaries of new research papers, so you don't miss out on anything new in the field and you can stay up to date with all the hot topics in the field. Furthermore, Spandex focuses on cutting-edge technologies and frameworks.

YouTube is a great tool, but it's just one of many. Combine it with online courses on Coursera, reading books and related articles on data science, participating in Kaggle competitions, and even contributing to open source projects. This holistic approach will not only solidify your knowledge, but also help you build your portfolio and stay sharp in the dynamic world of data science.