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- Free Courses
- Statistics free courses
Free Statistics Courses
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MIT IDSS
12 weeks  • Online
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Free Statistics Courses
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Skills: Measures of Central Tendency, Type of Statistics
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Skills: Types of Questions, Conditional Probability
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Skills: Introduction to Fourier Series, Dirichlet's conditions for Fourier Series, Useful Integration formulas, Fourier Series Examples, Half Range Fourier Series
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Skills: Partial Differential Equation
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Skills: Frequency Distribution Table
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Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples
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Skills: Probability Distribution, Normal Distribution
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Skills: Measures of Dispersion, Statistics
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Skills: Central Limit Theorem, Hypothesis Test
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Skills: Introduction to L'Hospital's Rule, Working for L’Hospital’s Rule, Solving multiple problems using L'Hospital's Rule
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Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations
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Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems
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Skills: Measures of Central Tendency, Type of Statistics
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Skills: Types of Questions, Conditional Probability
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Skills: Introduction to Fourier Series, Dirichlet's conditions for Fourier Series, Useful Integration formulas, Fourier Series Examples, Half Range Fourier Series
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Skills: Partial Differential Equation
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Skills: Frequency Distribution Table
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Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples
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Skills: Probability Distribution, Normal Distribution
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Skills: Measures of Dispersion, Statistics
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Skills: Central Limit Theorem, Hypothesis Test
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Skills: Introduction to L'Hospital's Rule, Working for L’Hospital’s Rule, Solving multiple problems using L'Hospital's Rule
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Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations
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Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems
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Learn Statistics For Free
These free statistics courses cover everything from foundational statistics to practical analysis methods, giving you a clear learning path for data science, analytics, and machine learning. Whether you are starting with probability, populations and samples, descriptive statistics, and statistical distributions, or building stronger skills in hypothesis testing, regression analysis, and decision-making methods, these courses teach the statistical concepts needed to understand data more accurately and support better analytical thinking.
Starting with core concepts, you will learn how to summarize data, interpret variability, study relationships through correlation, and apply inferential methods such as hypothesis testing, chi-square tests, ANOVA, and the central limit theorem. As you progress, you will strengthen your ability to use statistics in exploratory data analysis, machine learning, and real decision-making scenarios, helping you move from reading data to drawing clearer, more reliable conclusions.
Skills You’ll Gain in These Best Free Statistics Courses
Descriptive Statistics: Measures of central tendency (mean, median, mode) and dispersion.
Probability: Basic probability rules, normal distribution, and sampling distributions.
Inferential Statistics: Confidence intervals, hypothesis testing, and regression analysis.
- Data Analysis Tools: Courses often use software such as R, Python, or Excel.
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Skills: Chi-Square Test, Hypothesis Testing, Measures of Dispersion, Inferential Statistics
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Skills: Frequency Distribution Table
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Skills: Central Limit Theorem, Hypothesis Test
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Skills: Probability Distribution, Normal Distribution
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Skills: Introduction to ANOVA, Important Terminologies in ANOVA, Understanding Hypothesis Testing, One Way and Two Way ANOVA, Understanding MANOVA
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Skills: Introduction to Sensitivity Analysis, Types of Sensitivity Analysis, How Does Sensitivity Analysis Work?, Key Applications of Sensitivity Analysis, Advantages and Disadvantages, Practical Demonstration in Python
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Skills: Data Collection, Statistical Analysis, Probability, Central Limit Theorem, Hypothesis Testing, Chi-Square Test, ANOVA
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Skills: Measures of Central Tendency, Type of Statistics
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Skills: Taylor series, Multiple examples of solving problems
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Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations
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Skills: Measures of Dispersion, Statistics
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Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems
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Skills: Partial Differential Equation
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Skills: Differentiation and integration of Laplace Transform, Introduction to Inverse Laplace Transform, First Shifting Theorem, Examples using Partial Fractions, Convolution theorem and examples
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Skills: Introduction to Jacobians, Solving multiple Jacobian problems
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Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples
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Skills: Descriptive statistics, probability theory, hypothesis testing, regression analysis, decision making methods
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Skills: Set Theory, Relations, Functions
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Skills: Introduction to Fourier Series, Dirichlet's conditions for Fourier Series, Useful Integration formulas, Fourier Series Examples, Half Range Fourier Series
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Skills: Probability Theory, Introduction to probability, Rules for probability calculation, Bayes Theorem, Normal Distribution
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Skills: Central Tendency, Measures of Variability, Measure of Skewness, Kurtosis
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Skills: Types of Questions, Conditional Probability
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New
Skills: Chi-Square Test, Hypothesis Testing, Measures of Dispersion, Inferential Statistics
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Skills: Frequency Distribution Table
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Skills: Central Limit Theorem, Hypothesis Test
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Skills: Probability Distribution, Normal Distribution
View Course
Skills: Introduction to ANOVA, Important Terminologies in ANOVA, Understanding Hypothesis Testing, One Way and Two Way ANOVA, Understanding MANOVA
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Skills: Introduction to Sensitivity Analysis, Types of Sensitivity Analysis, How Does Sensitivity Analysis Work?, Key Applications of Sensitivity Analysis, Advantages and Disadvantages, Practical Demonstration in Python
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Skills: Data Collection, Statistical Analysis, Probability, Central Limit Theorem, Hypothesis Testing, Chi-Square Test, ANOVA
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Skills: Measures of Central Tendency, Type of Statistics
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Trending
Skills: Taylor series, Multiple examples of solving problems
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Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations
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Skills: Measures of Dispersion, Statistics
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Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems
View Course
Skills: Partial Differential Equation
View Course
Skills: Differentiation and integration of Laplace Transform, Introduction to Inverse Laplace Transform, First Shifting Theorem, Examples using Partial Fractions, Convolution theorem and examples
View Course
Skills: Introduction to Jacobians, Solving multiple Jacobian problems
View Course
Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples
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Popular
Skills: Descriptive statistics, probability theory, hypothesis testing, regression analysis, decision making methods
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Skills: Set Theory, Relations, Functions
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Skills: Introduction to Fourier Series, Dirichlet's conditions for Fourier Series, Useful Integration formulas, Fourier Series Examples, Half Range Fourier Series
View Course
Skills: Probability Theory, Introduction to probability, Rules for probability calculation, Bayes Theorem, Normal Distribution
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Skills: Central Tendency, Measures of Variability, Measure of Skewness, Kurtosis
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Skills: Types of Questions, Conditional Probability
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Frequently Asked Questions
What will I learn in these free Statistics courses?
You will learn probability, populations and samples, statistical analysis, hypothesis testing, statistical distributions, descriptive statistics, inferential statistics, regression analysis, and exploratory data analysis. These topics help you build a strong base for data science, analytics, and machine learning work.
What core modules are covered across the overall learning path?
The overall path covers probability, central tendency, variability, skewness, kurtosis, statistical distributions, sampling, hypothesis testing, correlation analysis, regression analysis, chi-square tests, ANOVA, and decision-making methods.
Will I learn descriptive statistics in a practical way?
Yes. You will study central tendency, measures of variability, skewness, and kurtosis, which help you summarize data clearly before moving to more advanced analysis.
Do these courses cover inferential statistics
Yes. The learning path includes inferential statistics topics such as data collection, probability, the central limit theorem, hypothesis testing, chi-square tests, and ANOVA.
What will I learn about hypothesis testing?
You will learn how hypotheses are used to test statements, along with the role of probability, sampling, and the central limit theorem in drawing conclusions from data.
Do these courses include decision-making methods?
Yes. Statistical Methods for Decision Making covers descriptive statistics, probability theory, hypothesis testing, regression analysis, and decision-making methods, which helps you use data more effectively in business and analytical contexts.
Will I learn statistics for machine learning?
Yes. The page includes courses on the importance of statistics in machine learning and statistics for machine learning, covering descriptive statistics, measures of dispersion, empirical and Chebyshev rules, and correlation analysis.
Will I learn exploratory data analysis?
Yes. Statistical Analysis includes statistical analysis and EDA, which helps you examine patterns, distributions, and relationships before modeling or deeper analysis.
What practical outcomes will I get from these Statistics courses
You will build the ability to summarize data, interpret variability, test assumptions, understand distributions, and support data-driven decisions in analytics, machine learning, and applied business problems.
Do these courses help with data science and analytics work?
Yes. Great Learning states that these courses help you work on data science and machine learning tasks, implement statistical methods, and make data-driven managerial decisions.
Are there prerequisites for these Statistics courses?
No. Great Learning says these courses have no prerequisites and that anybody can learn from them online for free.
Who should take these Statistics courses?
These courses are useful for beginners, learners moving into data science or analytics, and anyone who wants a stronger grasp of data interpretation, statistical thinking, and machine learning foundations. Great Learning also notes that statistics supports roles such as data analyst, data scientist, market researcher, investment analyst, and statistician.
What are the steps to enroll in these free Statistics courses?
To learn Statistics from these courses, you need to,
- Go to the course page
- Click on the "Enroll for Free" button
- Start learning the Statistics course for free online.
Who are eligible to take these free Statistics courses?
These courses have no prerequisites. Anybody can learn from these courses for free online.
Why take Statistics courses from Great Learning Academy?
Great Learning Academy offers a wide range of high-quality, completely free Statistics courses. From beginner to advanced level, these free courses are designed to help you improve your Data Science and Business analytics skills and achieve your goals. All these courses come with a certificate of completion, so you can demonstrate your new skills to the world. Start learning today and discover the benefits of free Statistics courses!
How much do these Statistics courses cost?
These are free courses, and you can enroll in them and learn for free online.
Will I get a certificate after completing these free Statistics courses?
All courses are free, A certificate is available for a nominal fee upon successful completion of the course.
What knowledge and skills will I gain upon completing these free Statistics courses?
You will gain a foundational understanding of Statistics. You will be skillful in working with Data Science and Machine Learning tasks, including implementing statistical methods and deriving data-driven managerial decisions. You will realize the importance of statistics in various sectors and learn to apply it in the FinTech industry.
How long does it take to complete these Statistics courses?
These courses include 2-8 hours of video lectures. These courses are, however, self-paced, and you can complete them at your convenience.
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