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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 Data Science Youtube Channels to Follow in 2024

Nitika Sharma
Nitika Sharma Last Updated : 21 Nov, 2024
9 min read

Data science is a rapidly growing field that combines programming, statistics, and domain expertise to extract insights and knowledge from data. Many resources are available for learning data science, including online courses, textbooks, and blogs. In this article, we will focus on YouTube channels that offer free data science learning. These data science youtube channels are a great way to get started with career or supplement your existing knowledge.

I have selected the top 10 YouTube channels based on their content quality, popularity, and the variety of topics covered. These channels offer a mix of lectures, tutorials, and demonstrations on data science concepts and tools.

Read about this article Learning Path to Become a Data Scientist 

Let’s get started with our list of the top 10 YouTube channels for free data science learning!

Table of contents

10 Best Data Science YouTube Channels

3Blue1Brown

3Blue1Brown , data science youtube channels

@3blue1brown | 5.62M subscribers | 150 videos

Are you struggling to understand complex math problems? Look no further than 3Blue1Brown! This YouTube channel, created by Grant Sanderson, uses animation to explain difficult concepts in a way that’s both easy to understand and entertaining. With over 5.6 million subscribers and 375 million views, 3Blue1Brown has become a go-to resource for anyone wanting to learn math or understand the workings of deep learning algorithms.

Instead of dry lectures and confusing equations, 3Blue1Brown uses animation to bring math to life. Grant’s engaging videos explain complex topics like linear algebra and calculus in a way that’s clear and easy to follow. He also delves into other areas like physics and computer science, making 3Blue1Brown a well-rounded resource for anyone interested in these fields.

Whether you’re a student struggling with their math homework or someone who simply wants to learn more about the world around you, 3Blue1Brown is a great place to start. So why not head over to the channel and check out some of Grant’s awesome videos? You might be surprised at how much fun learning math can be!

Click here to explore this data science youtube channel here.

Joma Tech

Joma Tech

@jomakaze | 2.27M subscribers | 98 videos

Data science professionals, navigating your career path and seeking insights into industry trends? Joma Tech is your perfect guide. Joma, a seasoned data scientist with experience at Big Tech companies like Facebook, LinkedIn, and Microsoft, offers invaluable expertise through his engaging YouTube channel.

His unique combination of knowledge and charisma makes his videos not only informative but also entertaining and engaging. Launched in 2016, his channel has garnered a loyal following of over 2.27M subscribers, drawn to his clear explanations of complex concepts and insightful discussions on the data science landscape.

Beyond theoretical knowledge, Joma dives deep into the practicalities of data science careers. He pulls back the curtain on the hiring process, detailing what employers look for and the realities of day-to-day work as a data scientist or engineer. Whether you’re a seasoned professional seeking to refine your skills or a newcomer embarking on your data science journey, Joma Tech offers valuable guidance and support.

You can explore this youtube channel for data science professionals here.

CS Dojo

CS Dojo

@CSDojo | 1.92M subscribers | 104 videos

Dive into the world of programming with CS Dojo, a YouTube channel by the enthusiastic and knowledgeable YK. Boasting 1.92 million subscribers and 80 million views, CS Dojo caters to both beginners and seasoned developers.

YK’s clear explanations and engaging style make even challenging concepts enjoyable to learn. Whether you’re just starting out or a pro, CS Dojo offers diverse content, from Python and JavaScript tutorials to deep dives into algorithms and data structures. For data scientists, CS Dojo provides valuable content to enhance programming skills, tackle algorithms, and gain insights into essential soft skills. Whether you’re starting your first project or leveling up, CS Dojo, with YK’s guidance, is your go-to resource.

CS Dojo goes beyond technicalities, covering essential soft skills for programmers. YK shares insights on career development, interview preparation, and common challenges. His passion for helping others shines through in every video.

You can access this data science youtube channel here.

Two Minute Papers

Two Minute Papers

@TwoMinutePapers | 1.51M subscribers | 848 videos

Stay at the forefront of AI advancements with Two Minute Papers, a YouTube channel that offers succinct summaries of the latest research breakthroughs. With 1.51 million subscribers and over 133 million views across 848 videos, Two Minute Papers serves as a trusted resource in the fast-evolving realm of artificial intelligence.

True to its name, each video delivers concise insights, condensing complex academic papers into two-minute nuggets. This efficient format allows data professionals to stay informed without compromising time. Covering diverse AI topics, from deep learning to natural language processing, Two Minute Papers caters to various data science roles.

What sets Two Minute Papers apart is its engaging approach to demystifying complex research. Utilizing animation, humor, and clear explanations, the channel makes technical concepts understandable for data scientists of all backgrounds.

Beyond summarizing research, Two Minute Papers provides insightful analysis and commentary, exploring the implications for data science practices. This fosters a deeper understanding of research significance and encourages meaningful engagement.

Click here to explore this YouTube channel.

sentdex

sentdex | data science youtube channels

@sentdex | 1.28M subscribers | 1.2K videos

Embark on a comprehensive journey through Python and beyond with Sentdex, a YouTube channel known for its in-depth tutorials. Boasting over 1.28 million subscribers, Sentdex explores machine learning, finance, data analysis, robotics, web development, and game development. Whether a beginner or seasoned developer, Sentdex offers high-quality content. With a well-organized layout and a searchable website (pythonprogramming.net), finding information is easy.

Sentdex’s engaging presentation style, humor, and real-world examples make complex concepts understandable. His passion for the craft is evident in every video. Beyond teaching technical skills, Sentdex encourages critical thinking and problem-solving, making his content invaluable for real-world programming. Data scientists can get practical insights into Python applications for data analysis and machine learning.

Click here to explore this data science youtube channel.

Corey Schafer

Corey Schafer | data science youtube channels

@coreyms | 1.24M subscribers | 232 videos

Corey Schafer’s YouTube channel, with 1.24 million subscribers, 232 videos, and over 91 million views, is a goldmine of programming tutorials. Corey, a seasoned software developer with over a decade of experience, covers many programming languages like Python, Java, and C++. Whether you’re a novice embarking on your coding journey or a seasoned programmer aiming to broaden your skillset, Corey Schafer has tailored content for you.

The channel distinguishes itself with its clear and concise explanations, practical examples and hands-on exercises that facilitate applying acquired knowledge. Beyond the technical intricacies, Corey Schafer delves into valuable insights and advice, offering a nuanced glimpse into the world of programming with a subtle touch of data science.

What truly sets Corey Schafer apart is the meticulous attention to detail and care evident in each video. Corey demonstrates an unwavering commitment to delivering an optimal learning experience, from comprehensive explanations to high-quality production.

Click here to explore this data science youtube channel.

StatQuest with Josh Starmer

stat quest | data science youtube channels

@statquest | 1.05M subscribers | 266 videos

For anyone intimidated by the seemingly daunting world of statistics, machine learning, and data science, StatQuest with Josh Starmer offers a refreshing and accessible approach. Recognizing that these complex topics can feel overwhelming, Josh breaks them down into manageable pieces, building your understanding brick by brick. His philosophy? Even the most intricate concepts are merely a combination of simple steps, waiting to be unlocked.

Through engaging visuals and clear explanations, StatQuest illuminates even the most challenging aspects of data science. But don’t be fooled by the approachable style – Josh doesn’t shy away from the complexities of the subject. He aims to empower you with a deeper understanding, not just a superficial grasp of the concepts.

StatQuest boasts a wealth of resources, including over 266 videos covering a diverse range of topics, all accumulated since its inception in May 2011. With over 1.05 million subscribers and nearly 57 million views, the channel has established itself as a leading authority in data science education.

Whether you’re starting your data science journey or looking to improve your skills, StatQuest is a valuable resource. Its clear explanations, engaging visuals, and focus on learning make it an ideal platform to explore the field.

Click here to explore this youtube channel.

Siraj Raval

Siraj Raval | best youtube channel to learn data science

@SirajRaval | 761K subscribers | 465 videos

Siraj Raval, a passionate technologist, brings the complexities of artificial intelligence, mathematics, science, and technology to the fingertips of everyone. Through his YouTube channel, with over 761K subscribers, he aims to ignite a global movement of data literacy, empowering individuals to understand and utilize these powerful tools.

Siraj believes knowledge is key to building wealth, leading a happier life, and making a positive impact on the world. His infectious enthusiasm and clear explanations make even the most challenging concepts approachable and engaging. He is not just an educator, but a community builder, fostering a thriving AI community, the fastest growing of its kind.

With a deep sense of purpose, Siraj has entrusted the continuation of his mission to his community. Should anything happen to him, his legacy will live on through the vibrant ecosystem he has tirelessly cultivated. His dedication to spreading knowledge and empowering individuals serves as a beacon of hope for a future shaped by collaboration and technological advancement. 

Click here to explore this youtube channel.

Codebasics

codebasics | best youtube channel to learn data science

@codebasics | 900K subscribers | 748 videos

Start your data science journey with Codebasics, a top YouTube channel led by experts Dhaval Patel and Hemanand Vadivel. With over 900K subscribers and 64 million views, Codebasics is a popular resource for aspiring data scientists.

Dhaval, with over 12 years of industry experience, including roles at Bloomberg and NVIDIA, and Hemanand, with 6+ years in the European data scene, bring a wealth of real-world expertise to their teaching. Their passion for education shines through in clear, engaging explanations that demystify even the most complex concepts.

Whether you’re a beginner or looking to refine your skills, Codebasics covers a diverse range of content, including fundamental coding languages, data science and analytics concepts, artificial intelligence, machine learning, and data structures. The channel goes beyond theory, emphasizing practical applications to prepare you for real-world data science challenges.

Click here to access this youtube channel.

Analytics Vidhya

Analytics Vidhya | best youtube channel to learn data science

@Analyticsvidhya | 47.1K subscribers | 805 videos

Analytics Vidhya is a community-driven platform that empowers aspiring and established professionals in analytics, data science and generative AI. Our mission is simple: equip you with the knowledge and skills needed to extract valuable insights from raw data, ultimately propelling you towards informed decision-making.

With over 47,000 subscribers and 805 videos boasting nearly 2 million views, Analytics Vidhya has established itself as a trusted resource for data enthusiasts. Whether you’re a seasoned professional or taking your first steps in the world of analytics, our diverse content library caters to all levels of expertise.

Dive into our extensive collection of videos covering a range of topics, from mastering SQL queries to tackling complex machine learning algorithms. Explore their data science project tutorials, gaining practical experience by building your portfolio. Engage with a vibrant community, exchanging ideas and seeking guidance from fellow data enthusiasts.
With Analytics Vidhya, you unlock the doors to a world of possibilities, empowering yourself to master the data-driven age.

Conclusion

In conclusion, these top 10 data science YouTube channels offer educational resources for learners of all levels. From detailed video tutorials to interactive live streams, they provide valuable content to enhance your skills and career. Whether you are a beginner learning the basics or an experienced data scientist seeking to grow, there’s a channel here for you. Check out these top channels for data science and subscribe to stay updated on the latest trends.

Frequently Asked Questions

Q1.Which YouTube channel is best for data science?

Channels like Krish Naik, StatQuest with Josh Starmer, and Ken Jee are highly recommended for data science learning.

Q2. Can I learn data science through YouTube?

Yes, YouTube offers free and valuable resources to learn data science, including tutorials, projects, and practical examples.

Q3. What is the best YouTube channel for science?

Channels like Kurzgesagt – In a Nutshell, Veritasium, and SciShow are excellent for exploring science topics.

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Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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