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Reading list

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
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 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

5 Best YouTube Channels to Learn Statistics for Free

ayushi9821704
ayushi9821704 28 Aug, 2024
5 min read

Introduction

Statistics is not limited to academic or specific profession. No matter whether you need to study data sciexnce, do some research or just try to handle the information about yourself, you need to understand statistics. Thanks to the development of the internet and particularly the distance learning concept, YouTube has proved to be a place with a lot of free learning materials. However, as seen, several channels are available and it may be challenging to filter the best channel depending on the content to be aired. In this article, you will discover five of the most useful YouTube channels that teach statistics for free and with great quality to become a master in this essential subject.

Learning Outcomes

  • Identify the top YouTube channels for learning statistics for free.
  • Understand the unique teaching styles and content offerings of each channel.
  • Learn how these channels can cater to different learning needs, from beginners to advanced students.
  • Discover practical applications of statistics through various teaching methods.
  • Find the best channel that suits your learning style and educational goals.

Table of contents

5 Best YouTube Channels to Learn Statistics for Free

Let us now look into each YouTube channels below in detail which is best to learn statistics for free.

1. Khan Academy

Khan Academy is a well-known educational platform that offers a wide range of subjects, including a comprehensive statistics curriculum. Their statistics playlist covers everything from basic concepts to more complex topics, making it an excellent resource for learners at any level.

Khan Academy: Learn Statistics for Free

Key Topics Covered:

  • Descriptive statistics
  • Probability
  • Inferential statistics
  • Hypothesis testing
  • Regression analysis

Unique Features: Khan Academy’s lessons are designed with a self-paced learning approach, allowing you to progress at your own speed. The interactive exercises and quizzes also help reinforce your understanding.

Best For: Beginners and intermediate learners looking for a structured, step-by-step guide to statistics.

@khanacademy | 8.52M subscribers | 8.5K videos

Click here to access the YouTube channel.

2. StatQuest with Josh Starmer

StatQuest is a channel created by Josh Starmer who is a statistician who makes statistics appealing and relatively simple to comprehend. His videos explain the concepts which are statistical at high level, in simple and understandable chunks.

StatQuest with Josh Starmer: Learn Statistics for Free

Key Topics Covered:

Unique Features: One of the standout features of StatQuest is Josh’s ability to simplify advanced topics without losing their essence. His friendly and enthusiastic teaching style keeps viewers engaged and motivated to learn.

Best For: Learners who appreciate a hands-on, approachable teaching style and are looking for clarity on both basic and advanced statistical concepts.

@statquest | 1.23M subscribers | 281 videos

Click here to access the YouTube channel.

3. Brandon Foltz

Brandon Foltz’s YouTube channel is a hidden gem for students who want to understand the practical applications of statistics. He focuses on business statistics, making his content particularly useful for those in business, economics, and related fields.

Brandon Foltz: Learn Statistics for Free

Key Topics Covered:

  • Basic and advanced statistics
  • Probability theory
  • Econometrics
  • SPSS tutorials
  • Business statistics

Unique Features: Brandon’s videos are detailed and often longer than typical YouTube tutorials, providing an in-depth look at each topic. He also solves problems step-by-step, which is beneficial for those who need to see the application of theory.

Best For: Business students, professionals, or anyone interested in learning how statistics are used in real-world scenarios.

@BrandonFoltz | 291K subscribers | 411 videos

Click here to access the YouTube channel.

4. FreeCodeCamp.org

FreeCodeCamp.org is widely known for its coding tutorials, but it also offers excellent content on statistics and probability, particularly within the context of data science. Their statistics playlists are geared towards practical application, often linked to programming and data analysis.

FreeCodeCamp.org

Key Topics Covered:

Unique Features: The integration of statistics with programming makes FreeCodeCamp.org’s content particularly valuable for learners looking to apply statistical methods to data science projects. The channel’s long-form tutorials are comprehensive and practical.

Best For: Data science students and professionals who want to learn statistics alongside coding and data analysis.

@freecodecamp | 9.86M subscribers | 1.7K videos

Click here to access the YouTube channel.

5. Zed Statistics

Zed Statistics is a treasure trove for anyone keen on understanding the intricate details of statistical concepts. The channel is run by a passionate statistician who breaks down complex ideas into digestible, entertaining lessons. Zed Statistics uses animations and clear explanations, making even the most daunting topics accessible to learners.

Zed Statistics

Key Topics Covered:

  • Statistical distributions
  • Hypothesis testing
  • Confidence intervals
  • Regression analysis
  • ANOVA (Analysis of Variance)

Unique Features:
Zed Statistics stands out for its engaging use of visuals and storytelling. The channel’s creator is known for making statistics not just understandable but also enjoyable. The focus is on creating a solid foundation in statistics, which is beneficial for both students and professionals.

Best For:
Learners who prefer a visually engaging and animated approach to understanding statistics, especially those who want a clear and detailed explanation of fundamental concepts.

@zedstatistics | 238K subscribers | 83 videos

Click here to access the YouTube channel.

Conclusion

We have compiled the list of 5 useful YouTube channels for learning statistics for free. No matter if you are a beginner or want to expand your knowledge. Starting from the properly articulated and most formally constructed Khan Academy all the way to the graphic and appealing 3Blue1Brown every single channel has something different to offer. Check these resources, discover the one you like most, and boost your statistics knowledge – all for free.

Frequently Asked Questions

Q1: Which channel is best for beginners?

A: Khan Academy is ideal for beginners due to its structured, step-by-step approach.

Q2: Can I learn statistics for data science on these channels?

A: Yes, channels like FreeCodeCamp.org offer statistics content specifically geared towards data science.

Q3: Are these channels suitable for exam preparation?

A: Absolutely, channels like Khan Academy and Brandon Foltz offer content that is perfect for preparing for exams like SAT, AP Statistics, and GRE.

Q4: Do these channels cover practical applications of statistics?

A: Yes, channels like StatQuest and zedstatistics focus on practical applications, particularly in data science and machine learning.

data analysislearn statistics for freepythonstatistics
ayushi9821704
ayushi9821704 28 Aug, 2024

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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