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University & Pro Programs

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MIT IDSS

12 weeks  • Online

Learn from MIT Faculty
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Statistics for Data Science & Analytics
40 coding exercises 3 projects

Free Statistics Courses

img icon BASICS
Measures of Central Tendency
star   4.43 3.8K+ learners 1.5 hrs

Skills: Measures of Central Tendency, Type of Statistics

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Probability Basics
star   4.5 9.5K+ learners 1 hr

Skills: Types of Questions, Conditional Probability

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Introduction to Fourier Series
star   4.46 16.1K+ learners 2.5 hrs

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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Partial Differential Equation
star   4.4 5.7K+ learners 2.5 hrs

Skills: Partial Differential Equation

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Frequency Distribution
star   4.54 1.6K+ learners 1 hr

Skills: Frequency Distribution Table

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Laplace Transformation
star   4.34 9K+ learners 1 hr

Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples

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Probability and Normal Distribution
star   4.39 2.3K+ learners 1.5 hrs

Skills: Probability Distribution, Normal Distribution

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Measures of Dispersion
star   4.48 2.3K+ learners 1 hr

Skills: Measures of Dispersion, Statistics

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Central Limit Theorem
star   4.44 1.6K+ learners 2 hrs

Skills: Central Limit Theorem, Hypothesis Test

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L'Hospital's rule
star   4.2 6.4K+ learners 1.5 hrs

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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Cauchy-Riemann Equations
star   4.5 2.6K+ learners 1 hr

Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations

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Euler’s method
star   4.41 4.5K+ learners 1 hr

Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems

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Measures of Central Tendency
star   4.43 3.8K+ learners 1.5 hrs

Skills: Measures of Central Tendency, Type of Statistics

free icon BASICS
Probability Basics
star   4.5 9.5K+ learners 1 hr

Skills: Types of Questions, Conditional Probability

free icon BASICS
Introduction to Fourier Series
star   4.46 16.1K+ learners 2.5 hrs

Skills: Introduction to Fourier Series, Dirichlet's conditions for Fourier Series, Useful Integration formulas, Fourier Series Examples, Half Range Fourier Series

free icon BASICS
Partial Differential Equation
star   4.4 5.7K+ learners 2.5 hrs

Skills: Partial Differential Equation

free icon BASICS
Frequency Distribution
star   4.54 1.6K+ learners 1 hr

Skills: Frequency Distribution Table

free icon BASICS
Laplace Transformation
star   4.34 9K+ learners 1 hr

Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples

free icon BASICS
Probability and Normal Distribution
star   4.39 2.3K+ learners 1.5 hrs

Skills: Probability Distribution, Normal Distribution

free icon BASICS
Measures of Dispersion
star   4.48 2.3K+ learners 1 hr

Skills: Measures of Dispersion, Statistics

free icon BASICS
Central Limit Theorem
star   4.44 1.6K+ learners 2 hrs

Skills: Central Limit Theorem, Hypothesis Test

free icon BASICS
L'Hospital's rule
star   4.2 6.4K+ learners 1.5 hrs

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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Cauchy-Riemann Equations
star   4.5 2.6K+ learners 1 hr

Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations

free icon BASICS
Euler’s method
star   4.41 4.5K+ learners 1 hr

Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems

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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Get started with these courses

img icon BASICS
Introduction to Statistical Tests
1.3K+ learners 7 hrs

Skills: Chi-Square Test, Hypothesis Testing, Measures of Dispersion, Inferential Statistics

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Frequency Distribution
star   4.54 1.6K+ learners 1 hr

Skills: Frequency Distribution Table

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Central Limit Theorem
star   4.44 1.6K+ learners 2 hrs

Skills: Central Limit Theorem, Hypothesis Test

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Probability and Normal Distribution
star   4.39 2.3K+ learners 1.5 hrs

Skills: Probability Distribution, Normal Distribution

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Analysis of Variance
star   4.56 4.5K+ learners 1 hr

Skills: Introduction to ANOVA, Important Terminologies in ANOVA, Understanding Hypothesis Testing, One Way and Two Way ANOVA, Understanding MANOVA

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Sensitivity Analysis
star   4.55 1.7K+ learners 1 hr

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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Inferential Statistics
star   4.55 4.8K+ learners 1 hr

Skills: Data Collection, Statistical Analysis, Probability, Central Limit Theorem, Hypothesis Testing, Chi-Square Test, ANOVA

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Measures of Central Tendency
star   4.43 3.8K+ learners 1.5 hrs

Skills: Measures of Central Tendency, Type of Statistics

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Taylor Series
1.4K+ learners 1 hr

Skills: Taylor series, Multiple examples of solving problems

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Cauchy-Riemann Equations
star   4.5 2.6K+ learners 1 hr

Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations

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Measures of Dispersion
star   4.48 2.3K+ learners 1 hr

Skills: Measures of Dispersion, Statistics

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Euler’s method
star   4.41 4.5K+ learners 1 hr

Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems

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Partial Differential Equation
star   4.4 5.7K+ learners 2.5 hrs

Skills: Partial Differential Equation

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Inverse Laplace Transformation
star   4.35 3.2K+ learners 2.5 hrs

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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Elaborate Jacobians
1.6K+ learners 1.5 hrs

Skills: Introduction to Jacobians, Solving multiple Jacobian problems

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Laplace Transformation
star   4.34 9K+ learners 1 hr

Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples

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Statistical Methods for Decision Making
star   4.44 65K+ learners 2 hrs

Skills: Descriptive statistics, probability theory, hypothesis testing, regression analysis, decision making methods

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Statistical Analysis
star   4.5 19.5K+ learners 1 hr

Skills: Statistical Analysis, EDA

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Discrete Mathematics Part 1
star   4.43 16.7K+ learners 2.5 hrs

Skills: Set Theory, Relations, Functions

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Introduction to Fourier Series
star   4.46 16.1K+ learners 2.5 hrs

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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Statistical Learning
star   4.48 15.6K+ learners 2.5 hrs

Skills: Probability Theory, Introduction to probability, Rules for probability calculation, Bayes Theorem, Normal Distribution

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Introduction to Descriptive Statistics
star   4.46 10.4K+ learners 1 hr

Skills: Central Tendency, Measures of Variability, Measure of Skewness, Kurtosis

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Hypothesis Testing
star   4.5 9.8K+ learners 2 hrs

Skills: Hypothesis Testing, T-test

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Probability Basics
star   4.5 9.5K+ learners 1 hr

Skills: Types of Questions, Conditional Probability

New

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Introduction to Statistical Tests
1.3K+ learners 7 hrs

Skills: Chi-Square Test, Hypothesis Testing, Measures of Dispersion, Inferential Statistics

img icon BASICS
Frequency Distribution
star   4.54 1.6K+ learners 1 hr

Skills: Frequency Distribution Table

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Central Limit Theorem
star   4.44 1.6K+ learners 2 hrs

Skills: Central Limit Theorem, Hypothesis Test

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Probability and Normal Distribution
star   4.39 2.3K+ learners 1.5 hrs

Skills: Probability Distribution, Normal Distribution

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Analysis of Variance
star   4.56 4.5K+ learners 1 hr

Skills: Introduction to ANOVA, Important Terminologies in ANOVA, Understanding Hypothesis Testing, One Way and Two Way ANOVA, Understanding MANOVA

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Sensitivity Analysis
star   4.55 1.7K+ learners 1 hr

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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Inferential Statistics
star   4.55 4.8K+ learners 1 hr

Skills: Data Collection, Statistical Analysis, Probability, Central Limit Theorem, Hypothesis Testing, Chi-Square Test, ANOVA

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Measures of Central Tendency
star   4.43 3.8K+ learners 1.5 hrs

Skills: Measures of Central Tendency, Type of Statistics

Trending

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Taylor Series
1.4K+ learners 1 hr

Skills: Taylor series, Multiple examples of solving problems

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Cauchy-Riemann Equations
star   4.5 2.6K+ learners 1 hr

Skills: Introduction to Cauchy-Riemann equations , Solving multiple problems using Cauchy-Riemann equations

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Measures of Dispersion
star   4.48 2.3K+ learners 1 hr

Skills: Measures of Dispersion, Statistics

img icon BASICS
Euler’s method
star   4.41 4.5K+ learners 1 hr

Skills: Euler’s Method Theory, Modified Euler’s Method, Working of Euler’s Method , Multiple examples of solving problems

img icon BASICS
Partial Differential Equation
star   4.4 5.7K+ learners 2.5 hrs

Skills: Partial Differential Equation

img icon BASICS
Inverse Laplace Transformation
star   4.35 3.2K+ learners 2.5 hrs

Skills: Differentiation and integration of Laplace Transform, Introduction to Inverse Laplace Transform, First Shifting Theorem, Examples using Partial Fractions, Convolution theorem and examples

img icon BASICS
Elaborate Jacobians
1.6K+ learners 1.5 hrs

Skills: Introduction to Jacobians, Solving multiple Jacobian problems

img icon BASICS
Laplace Transformation
star   4.34 9K+ learners 1 hr

Skills: Introduction to Laplace Transformation, Laplace transform using First Shifting Theorem, Multiple examples

Popular

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Statistical Methods for Decision Making
star   4.44 65K+ learners 2 hrs

Skills: Descriptive statistics, probability theory, hypothesis testing, regression analysis, decision making methods

img icon BASICS
Statistical Analysis
star   4.5 19.5K+ learners 1 hr

Skills: Statistical Analysis, EDA

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Discrete Mathematics Part 1
star   4.43 16.7K+ learners 2.5 hrs

Skills: Set Theory, Relations, Functions

img icon BASICS
Introduction to Fourier Series
star   4.46 16.1K+ learners 2.5 hrs

Skills: Introduction to Fourier Series, Dirichlet's conditions for Fourier Series, Useful Integration formulas, Fourier Series Examples, Half Range Fourier Series

img icon BASICS
Statistical Learning
star   4.48 15.6K+ learners 2.5 hrs

Skills: Probability Theory, Introduction to probability, Rules for probability calculation, Bayes Theorem, Normal Distribution

img icon BASICS
Introduction to Descriptive Statistics
star   4.46 10.4K+ learners 1 hr

Skills: Central Tendency, Measures of Variability, Measure of Skewness, Kurtosis

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Hypothesis Testing
star   4.5 9.8K+ learners 2 hrs

Skills: Hypothesis Testing, T-test

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Probability Basics
star   4.5 9.5K+ learners 1 hr

Skills: Types of Questions, Conditional Probability

Our learners also choose

Learner reviews of the Free Statistics Courses

Our learners share their experiences of our courses

4.46
70%
20%
6%
1%
3%
Reviewer Profile

5.0

Country Flag India
“An Engaging and Informative Course Experience”
I thoroughly enjoyed this course and found it to be highly informative and engaging. The curriculum was well-structured, covering a wide range of topics in depth. The instructors were knowledgeable and presented the material in a clear and concise manner. The quizzes and assignments were challenging yet fair, helping to reinforce the concepts learned. Overall, this course provided a comprehensive learning experience that was both enjoyable and educational.
Reviewer Profile
Manahil Tariq

5.0

“I learned about making boxplots using Seaborn and about discrete and continuous data. I am glad to know about prediction concepts for future thoughts.”
I am glad to be a part of this course. This increased my statistical concepts as much as I expected. I am very satisfied with this course.
Reviewer Profile

5.0

Country Flag United States
“Everything Seems to Be Great and Understandable”
I really enjoyed the clarity of the explanations and the detailed examples provided. The content was well-structured, making it easy to follow and apply the concepts effectively. The interactive examples and practical applications added a lot of value. Overall, it was engaging and informative!
Reviewer Profile

4.0

“Statistical Analysis: Outstanding Experience”
The course is put together very well. Support from the Program Manager has been good. Instructors are very qualified and explain the concepts very well.
Reviewer Profile

5.0

Country Flag Philippines
“Very good experience, great for beginners”
Lessons are very adequate to enhance our ability into proficient ones.
Reviewer Profile

5.0

“Student in International University”
The online data analysis course provided a solid foundation for understanding and applying essential concepts in the field. The content was well-structured, starting with basic statistical principles and gradually moving toward more advanced techniques like data visualization and predictive modeling. The inclusion of hands-on exercises and case studies made the lessons engaging and practical, helping to bridge the gap between theory and real-world application.
Reviewer Profile

5.0

Country Flag Saudi Arabia
“Clear Explanations Enhance Understanding of Complex Topics”
I appreciated the clear explanations that made complex topics accessible. The engaging format kept my interest throughout, and I felt more confident in my understanding. The balance of depth and simplicity made the learning experience enjoyable and effective.
Reviewer Profile

5.0

Country Flag India
“Excellent! Learning Experience and Enjoyed a Lot”
Amazing experience, very well explained along with good real-time examples! Kudos to Great Learning.
Reviewer Profile

5.0

Country Flag India
“Statistical Methods of Decision Making”
Clearly explained each and every topic in this course on statistical methods for decision making.
Reviewer Profile

5.0

Country Flag India
“Practical Applications and Interactive Discussions”
I loved the course; the way every module was made was amazing.

Meet your faculty

Meet industry experts who will teach you relevant skills in artificial intelligence

instructor img

Dr. P K Viswanathan

Professor, Analytics & Operations
Dr. P K Viswanathan, currently serves as a professor of analytics at Great Lakes Institute of Management. He teaches subjects such as business statistics, operations research, business analytics, predictive analytics, ML analytics, spreadsheet modeling and others. In the industrial tenure spanning over 15 years, he has held senior management positions in Ballarpur Industries (BILT) of the Thapar Group and the JK Industries of the JK Organisation. Apart from executing corporate consultancy assignments, Dr. PK Viswanathan has also designed and conducted training programs for many leading organizations in India. He has degrees in MSc (Madras), MBA (FMS, Delhi), MS (Manitoba, Canada), PHD (Madras).   Noteworthy achievements: Ranked 12th in the "20 Most Prominent Analytics & Data Science Academicians In India: 2018". Current Academic Position: Professor of Analytics, Great Lakes Institute of Management. Prominent Credentials: He has authored a total of four books, three of which are on Business Statistics and one on Marketing Research published by the British Open University Business School, UK. Research Interest: Analytics, ML, AI. Patents: He has original research publications exclusively on analytics where he has developed modeling and demonstrated their decision support capabilities. These are: Modelling Credit Default in Microfinance — An Indian Case Study, PK Viswanathan, SK Shanthi, Modelling Asset Allocation and Liability Composition for Indian Banks. Teaching Experience: He has been teaching analytics for more than two decades but has been into active and intense teaching since analytics started witnessing a meteoric growth with the advent of R and Python. Ph.D. in the application of Operations Research from Madras University.
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Mr. Bharani Akella

Data Scientist
Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.
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Dr. Abhinanda Sarkar

Senior Faculty & Director Academics, Great Learning
  • 30+ years of experience in data science, ML, and analytics.
  • Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

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,

  1. Go to the course page
  2. Click on the "Enroll for Free" button
  3. 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.