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Free Online Statistics & Probability Courses and Certifications 2024

Statistics & Probability is a top category of Mathematics that deals with the collection, analysis, interpretation, and presentation of data. It is a powerful tool for learning and understanding the world around us. With the help of online courses, students can gain a comprehensive understanding of the principles of Statistics & Probability and apply them to real-world problems. With the right knowledge, students can gain valuable insights into the world of data and make informed decisions.

Popular Courses

FA19: Statistical Modeling and Regression Analysis
FA19: Statistical Modeling and Regression Analysis
This course FA19: Statistical Modeling and Regression Analysis provides an introduction to the fundamentals of regression analysis. You will learn the basics of linear regression, logistic regression, Poisson regression, generalized linear regression and model selection. You will also gain experience in using the R statistical software to implement and interpret regression models. This course is designed to help you develop the skills to apply regression analysis to real-world data.
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Statistical Inference and Hypothesis Testing in Data Science Applications
Statistical Inference and Hypothesis Testing in Data Science Applications
This course will teach students about the theory and implementation of hypothesis testing in data science applications. Students will learn to use hypothesis tests to make informed decisions from data, including the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. The ethical implications of misuse of testing concepts will also be discussed. This course is part of the MS-DS degree offered on the Coursera platform.
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Studying Cities: Social Science Methods for Urban Research
Studying Cities: Social Science Methods for Urban Research
This course, Studying Cities: Social Science Methods for Urban Research, provides an introduction to the research cycle and the necessary tools to understand and evaluate key steps in scientific research. Through video lectures, in-video questions, and a final peer review, you will gain insight into the different steps of the research cycle, build a firm foundation for your own future research endeavors, and learn the components and criteria of the theoretical framework and operationalization, research strategies, and the collection and analysis of quantitative and qualitative data.
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Essentials of Data Science With R Software - 1: Probability and Statistical Inference
Essentials of Data Science With R Software - 1: Probability and Statistical Inference
This course provides an introduction to the essentials of data science with R software, focusing on probability and statistical inference. Students will learn the fundamentals of probability theory and statistical inference, and how to apply them to data analysis. They will also gain an understanding of how to use R software to implement these concepts and draw correct statistical conclusions.
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Causal Inference Bootcamp: Your Guide to Experiments
Causal Inference Bootcamp: Your Guide to Experiments
This Causal Inference Bootcamp is a comprehensive guide to experiments. It covers the Perry Preschool Project, the London Cholera Outbreak, and the effects of giving property rights to squatters. It also covers reading average treatment effects and confidence intervals, and the effects of depression in OHE. This course will help you understand the fundamentals of causal inference and how to apply them to experiments.
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An Introduction to Probability in Computing
An Introduction to Probability in Computing
This course provides an introduction to probability theory for computer science students. It covers the basics of probability, including random variables, distributions, and Bayesian inference. It also covers the application of probability theory to modern computer science topics such as machine learning, data mining, and artificial intelligence. Students will gain a better understanding of the role of probability in computing and be better prepared to tackle the challenges of modern computing.
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Accounting for Death in War: Separating Fact from Fiction
Accounting for Death in War: Separating Fact from Fiction
This course will teach you how to accurately account for death in war. You will learn about the methods used to calculate war deaths, and apply them to particular wars. You will also debunk some widely circulated war-death claims and explore non-violent deaths caused indirectly by war. This course is suitable for anyone interested in war, history, politics, and the use and abuse of numbers and statistics.
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Statistical Inference for Estimation in Data Science
Statistical Inference for Estimation in Data Science
This course introduces students to statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals. This course is part of the MS-DS degree offered on the Coursera platform, and is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
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Data Science: Probability
Data Science: Probability
This course introduces the fundamentals of probability theory and its application to data science. Learn how to use random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem to analyze data affected by chance. Gain the skills to conduct statistical tests and understand the risk of securities sold by financial institutions. This course is essential for data scientists and online learners.
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Statistics and R
Statistics and R
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference, analyze data with R code, use visualization techniques, and describe robust statistical techniques. The course is divided into seven parts, with topics ranging from simple calculations and descriptive statistics to advanced software engineering skills. We provide R programming examples to help make the connection between concepts and implementation. This course is self-paced and is part of two Professional Certificates.
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Combining and Analyzing Complex Data
Combining and Analyzing Complex Data
This course will teach you how to combine and analyze complex data using survey weights, linear and logistic regressions, record linkage, and statistical matching. You will learn how to use R® software to estimate descriptive statistics and model parameters. Additionally, the course will cover ethical issues related to combining datasets, such as obtaining informed consent from individuals. By the end of the course, you will have the skills to effectively analyze complex data.
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Data Analysis: Statistical Modeling and Computation in Applications
Data Analysis: Statistical Modeling and Computation in Applications
This course provides learners with the skills to analyze data and answer questions using real data. It covers foundational and practical skills such as hypothesis testing, regression, gradient descent methods, and domain knowledge. Learners will study common models and methods to analyze data in four different domain areas, and present their findings in written reports. This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an effective practitioner of data science.
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Statistical Predictive Modelling and Applications
Statistical Predictive Modelling and Applications
This course provides an introduction to statistical predictive modelling and its applications. You will learn linear and logistic regression, as well as naive Bayes, and how to apply them to real-world scenarios. Through online learning and skill training, you will gain the knowledge and skills to use these techniques to predict outcomes and make decisions.
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Introduction To R Software
Introduction To R Software
This course introduces students to the R software, a free software used for mathematical and statistical manipulations. It covers the basics of the R programming language and its built-in functions. Lectures and online learning will help students gain the skills needed to use the software for data analysis, simulations, and programming. The course is intended for UG students of Science and Engineering, students of humanities with basic mathematical background, and working professionals in analytics. A mathematics background up to class 12 is recommended. All industries involved in mathematical and statistical computations, programming, and simulations will benefit from this course.
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ANOVA and Experimental Design
ANOVA and Experimental Design
This course introduces students to the analysis of variance (ANOVA) and experimental design. Students will learn about linear regression models, randomization, blocking, factorial design, and causality. The course is part of the Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. This interdisciplinary degree is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
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Data Science: Inference and Modeling
Data Science: Inference and Modeling
This course will teach you the fundamentals of data science, including statistical inference and modeling, through a motivating case study on election forecasting. You will learn how to use R to define estimates and margins of errors, understand confidence intervals and p-values, and apply Bayesian modeling. At the end of the course, you will be able to recreate a simplified version of an election forecast model and apply it to the 2016 election.
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Frequently Asked Questions and Answers

Q1: What are descriptive statistics?

Descriptive statistics is a method of organizing, representing, and explaining a set of data using charts, graphs, and summary measures. Common methods for summarizing data and presenting it in tables or graphs include histograms, pie charts, bars, and scatter plots. Descriptive statistics serve the purpose of providing a description of the data.

Q2: What are summary statistics?

In Statistics, summary statistics are a component of descriptive statistics (which is one of the types of statistics) that provides a list of information about sample data. It is widely recognized that statistics involves the visual and quantitative presentation of data. Consequently, summary statistics focus on the summarization of statistical information.

Q3: What does statistics mean?

Statistics, derived from the German word "Statistik" meaning "description of a state, a country," is a discipline that encompasses the collection, organization, analysis, interpretation, and presentation of data.

Q4: What is the difference between population and statistics?

Populations are diverse groups of people or objects, such as "all people living in a country" or "every atom composing a crystal." Statistics encompasses every aspect of data, including the planning of data collection in terms of survey and experiment design.

Q5: What Statistics & Probability courses can I find on AZ Class?

On this page, we have collected free or certified 34 Statistics & Probability online courses from various platforms. The list currently only displays up to 50 items. If you have other needs, please contact us.

Q6: Can I learn Statistics & Probability for free?

Yes, If you don’t know Statistics & Probability, we recommend that you try free online courses, some of which offer certification (please refer to the latest list on the webpage as the standard). Wish you a good online learning experience!

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