Free Mathematics Courses with Certificates (2026)

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Great Learning Academy’s free mathematics courses cover data science and machine learning mathematics, discrete mathematics, probability, statistics, regression analysis, predictive modeling, relational algebra, and partial differential equations. Learn core quantitative concepts and practical mathematical techniques that support analytics, AI, technical interviews, and data-driven problem-solving. Build stronger reasoning and analytical skills to apply mathematics more confidently in real technical work.

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UNIVERSITY

Great Learning

Generative AI for Practitioners

12 weeks • Online

UNIVERSITY

Walsh College

MS in Artificial Intelligence & Machine Learning

2 Years • Online

UNIVERSITY

MIT IDSS

AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact

12 weeks • Online

UNIVERSITY

MIT Professional Education

No Code and Agentic AI

14 Weeks • Online

Learn from MIT Faculty

UNIVERSITY

Johns Hopkins University

Applied Generative AI and Agentic AI

16 weeks • Online

BASICS

4.34 16K+ learners 1 hr

Data Science Mathematics

Skills: Mathematics for Data Science, Case studies

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BASICS

4.42 32.8K+ learners 1.5 hrs

Mathematics for Job Interviews

Skills: Time Distance and Speed Problem, Age, Percentage Average, Profit and loss

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BASICS

4.43 16.8K+ learners 2.5 hrs

Discrete Mathematics Part 1

Skills: Set Theory, Relations, Functions

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BASICS

4.4 5.7K+ learners 2.5 hrs

Partial Differential Equation

Skills: Partial Differential Equation

View Course

BASICS

131 learners 1.5 hrs

Relational Algebra Essentials

Skills: Query Processing, Set Theory for Relational Algebra, Set theory operators, Selection, Projection, Rename, Unary operators, Binary operators, Cartesian product, Joins, Aggregation Operations, Complex Queries examples

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BASICS

4.58 43.8K+ learners 2 hrs

Statistics for Machine Learning

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

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BASICS

4.43 65.7K+ learners 2 hrs

Statistical Methods for Decision Making

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

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BASICS

Advanced Statistics for Machine Learning

4.49 11.4K+ learners 6 hrs

Advanced Statistics for Machine Learning

Skills: Advanced Statistics, Hypothesis testing, Type-I and Type-II error

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BASICS

4.48 15.7K+ learners 2.5 hrs

Statistical Learning

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

View Course

BASICS

4.47 55K+ learners 1.5 hrs

Probability for Data Science

Skills: Basics of Probability, Marginal Probability, Bayes Theorem

View Course

BASICS

4.53 30.4K+ learners 2.5 hrs

Regression Analysis Using R

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

View Course

BASICS

Data Science Mathematics

4.34 16K+ learners 1 hr

Skills: Mathematics for Data Science, Case studies

BASICS

Mathematics for Job Interviews

4.42 32.8K+ learners 1.5 hrs

Skills: Time Distance and Speed Problem, Age, Percentage Average, Profit and loss

BASICS

Discrete Mathematics Part 1

4.43 16.8K+ learners 2.5 hrs

Skills: Set Theory, Relations, Functions

BASICS

Partial Differential Equation

4.4 5.7K+ learners 2.5 hrs

Skills: Partial Differential Equation

BASICS

Relational Algebra Essentials

131 learners 1.5 hrs

BASICS

Statistics for Machine Learning

4.58 43.8K+ learners 2 hrs

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

BASICS

Statistical Methods for Decision Making

4.43 65.7K+ learners 2 hrs

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

BASICS

Advanced Statistics for Machine Learning

4.49 11.4K+ learners 6 hrs

Skills: Advanced Statistics, Hypothesis testing, Type-I and Type-II error

BASICS

Statistical Learning

4.48 15.7K+ learners 2.5 hrs

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

BASICS

Probability for Data Science

4.47 55K+ learners 1.5 hrs

Skills: Basics of Probability, Marginal Probability, Bayes Theorem

BASICS

Regression Analysis Using R

4.53 30.4K+ learners 2.5 hrs

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

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BASICS

131 learners 1.5 hrs

Relational Algebra Essentials

View Course

BASICS

4.4 5.7K+ learners 2.5 hrs

Partial Differential Equation

Skills: Partial Differential Equation

View Course

BASICS

4.49 11.4K+ learners 6 hrs

Advanced Statistics for Machine Learning

Skills: Advanced Statistics, Hypothesis testing, Type-I and Type-II error

View Course

BASICS

4.43 65.7K+ learners 2 hrs

Statistical Methods for Decision Making

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

View Course

BASICS

4.47 55K+ learners 1.5 hrs

Probability for Data Science

Skills: Basics of Probability, Marginal Probability, Bayes Theorem

View Course

BASICS

4.58 43.8K+ learners 2 hrs

Statistics for Machine Learning

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

View Course

BASICS

4.42 32.8K+ learners 1.5 hrs

Mathematics for Job Interviews

Skills: Time Distance and Speed Problem, Age, Percentage Average, Profit and loss

View Course

BASICS

4.53 30.4K+ learners 2.5 hrs

Regression Analysis Using R

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

View Course

BASICS

4.43 16.8K+ learners 2.5 hrs

Discrete Mathematics Part 1

Skills: Set Theory, Relations, Functions

View Course

BASICS

4.34 16K+ learners 1 hr

Data Science Mathematics

Skills: Mathematics for Data Science, Case studies

View Course

BASICS

4.48 15.7K+ learners 2.5 hrs

Statistical Learning

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

View Course

New

BASICS

131 learners 1.5 hrs

Relational Algebra Essentials

View Course

BASICS

4.4 5.7K+ learners 2.5 hrs

Partial Differential Equation

Skills: Partial Differential Equation

View Course

BASICS

4.49 11.4K+ learners 6 hrs

Advanced Statistics for Machine Learning

Skills: Advanced Statistics, Hypothesis testing, Type-I and Type-II error

View Course

Popular

BASICS

4.43 65.7K+ learners 2 hrs

Statistical Methods for Decision Making

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

View Course

BASICS

4.47 55K+ learners 1.5 hrs

Probability for Data Science

Skills: Basics of Probability, Marginal Probability, Bayes Theorem

View Course

BASICS

4.58 43.8K+ learners 2 hrs

Statistics for Machine Learning

Skills: Descriptive Statistics, Measures of Dispersion Range and IQR,,Central Tendency and 3 Ms,The Empirical Rule and Chebyshev Rule,Correlation Analysis

View Course

BASICS

4.42 32.8K+ learners 1.5 hrs

Mathematics for Job Interviews

Skills: Time Distance and Speed Problem, Age, Percentage Average, Profit and loss

View Course

BASICS

4.53 30.4K+ learners 2.5 hrs

Regression Analysis Using R

Skills: Linear Regression, Concept of Multicollinearity, R Square, Predictive Modeling

View Course

BASICS

4.43 16.8K+ learners 2.5 hrs

Discrete Mathematics Part 1

Skills: Set Theory, Relations, Functions

View Course

BASICS

4.34 16K+ learners 1 hr

Data Science Mathematics

Skills: Mathematics for Data Science, Case studies

View Course

BASICS

4.48 15.7K+ learners 2.5 hrs

Statistical Learning

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

View Course

Learner reviews of the Free Mathematics Courses

Our learners share their experiences of our courses

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Pratiksha Suresh Sangle

5.0

★★★★ ★

India

“I Learned It Because of Videos and Now I'm Happy After Learning Data Science and Mathematics”

I like the way of teaching because it makes learning easy, and I like the quizzes because they show how much attention we've paid while watching the learning videos.

Usman Haziq

5.0

★★★★ ★

“Data Science Math and Statistics Skills”

Basic mathematics skills such as algebra, calculus, probability, and statistics are fundamental for understanding data science and data analysis concepts. These skills enable data scientists to manipulate data, interpret statistical models, and make informed decisions based on data insights.

Muhammad SalekMiah

5.0

★★★★ ★

“Mathematics: The Core of Data Science”

Mathematics is the backbone of data science, driving critical processes like data analysis, algorithm development, and model optimization. Key areas such as statistics, linear algebra, probability, and calculus enable data scientists to interpret patterns, build predictive models, and derive meaningful insights. A solid understanding of these mathematical concepts is crucial for solving complex problems and making informed decisions in data-driven fields, making math an indispensable tool for data science success.

Tabasum Naz

5.0

★★★★ ★

“Data Science (DS) Is Crucial for Iterative Improvement”

It is crucial for monitoring model performance, as data patterns often shift over time (concept drift). Regular user and stakeholder feedback can lead to updates in the model, ensuring its continued relevance and accuracy. This feedback loop in DS is essential for fostering continuous improvement, advancing predictive capabilities, and ultimately driving data-driven decision-making within organizations.

Krithika M

5.0

★★★★ ★

India

“Machine Learning Was Taught Very Practically”

I liked the course so much; it gave me a lot of knowledge about data science.

Muhammad Zaid

5.0

★★★★ ★

“Easy to Follow and Great Quizzes and Assignments”

"Easy to follow" resources simplify complex concepts, making learning accessible for everyone. Engaging quizzes and assignments enhance understanding and retention, allowing learners to apply their knowledge effectively.

Vruddhi Shetty

5.0

★★★★ ★

India

“Insightful, Well-Structured, and Engaging”

Highly informative and well-organized course with practical examples. Great learning experience!

Kavya Ayappan

5.0

★★★★ ★

India

“Mathematical Foundations of Data Science: Essentials and Applications”

This course was very nice. The Data Science Mathematics course provides a solid foundation for understanding the mathematical underpinnings of data science and prepares you for more advanced topics in machine learning and data analysis.

Renuga Devi

5.0

★★★★ ★

India

“Data Science with Mathematics Is Very Useful”

The Data Science Mathematics course provides a solid foundation for understanding the mathematical underpinnings of data science and prepares you for more advanced topics in machine learning and data analysis.

Muhammad Asif

5.0

★★★★ ★

“Before Coming to This Website I Was a Little Confused About Some Points, Which Are Now Solved”

Excellent. I enjoyed all the content provided in this course.

Meet your faculty

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

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18+ years in AI, ML, and financial engineering solutions
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PhD in Mathematics from Pierre and Marie Curie University, France

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30+ years of experience in data science, ML, and analytics.
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Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

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.

Frequently Asked Questions

What will I learn in these free mathematics courses?

You will learn core mathematical and statistical concepts used in technical and data-driven work, including data science mathematics, discrete mathematics, probability, statistics, statistical learning, regression analysis, predictive modeling, relational algebra, and partial differential equations. These free .mathematics courses help you build stronger quantitative reasoning and problem-solving skills.

Are these free online math courses good for beginners?

Yes. These courses work well for beginners because they start with foundational topics such as percentages, averages, profit and loss, set theory, functions, probability, and descriptive statistics, then move into more applied topics used in analytics and machine learning.

What important topics are covered across these free math courses?

The topics include mathematics for data science, job interview math, set theory, relations, functions, query processing, joins, aggregation, probability theory, Bayes' theorem, normal distribution, regression analysis, hypothesis testing, Type I and Type II errors, and partial differential equations.

Will these courses help with data science and machine learning?

Yes. These courses cover probability, statistics, statistical learning, regression analysis, predictive modeling, and mathematics for data science. That makes them useful for learners who want stronger mathematical support for machine learning, analytics, and AI-related work.

Do these free online maths courses with certificates include probability and Bayes' theorem?

Yes. Probability is a key part of these courses. You will learn the basics of probability, marginal probability, Bayes' theorem, rules for calculating probabilities, and the normal distribution, which are important for statistical reasoning and machine learning

Will I learn statistics in these free math lessons online?

Yes. These courses cover descriptive statistics, measures of central tendency and dispersion, correlation analysis, hypothesis testing, and advanced statistical topics used in decision-making and machine learning.

Do these courses cover discrete mathematics?

Yes. Discrete mathematics is included through topics such as set theory, relations, and functions. These concepts are useful for logical reasoning, structured problem-solving, and many computer science and data-related applications.

Are regression analysis and predictive modeling included in these best online math courses free?

Yes. Regression analysis and predictive modeling are part of the overall coverage of mathematics. These topics help you understand how numerical relationships are modeled and how data can be used to make predictions.

Will I learn relational algebra in these free math classes?

Yes. These courses cover relational algebra topics such as query processing, set-theory operators, selection, projection, renaming, unary and binary operators, Cartesian product, joins, aggregation operations, and complex queries. This is helpful for learners who want stronger logic for database and query-based work.

Can these free online maths courses for adults help with interview preparation?

Yes. These courses include mathematics for job interviews, covering topics such as time, distance, speed, age, percentage, average, and profit and loss. This makes them useful for adults returning to study, preparing for aptitude rounds, or refreshing core math skills for job opportunities.

Will I get practical examples or case studies in these courses?

Yes. The overall mathematics courses include case studies and practical applications, especially in data science mathematics. This helps you connect abstract math concepts to real analytical and technical problems.

Do these courses include any tools, or are they only theory?

They are not only theory. Along with core concepts, these courses include applied analytical topics, and Regression Analysis Using R introduces regression and predictive modeling with R. This helps you connect mathematics to real analysis workflows.

What outcome can I expect after completing these free math lessons?

You can expect stronger quantitative reasoning, better problem-solving, greater confidence in statistics and probability, and a stronger foundation in data science, machine learning, analytics, interview preparation, and technical decision-making. These courses help turn mathematics from a weak spot into a useful working skill.