This article will discuss the Top 10 Free Online Courses in Statistics and the best free online statistics course with certificates. In previous decades, a career in statistics was almost always associated with a university academic position. On the other hand, data has become a critical component for successful operations due to several multi-disciplinary advances. Furthermore, the emergence of several sub-branches such as data science, big data, and machine learning has become increasingly important in organizations across industries to derive meaningful insights from the massive amounts of data generated every day.

Statistical experts are in high demand across various industries, including universities, sports professionals, finance, healthcare, engineering, technology, marketing and advertising, and e-commerce. The US Bureau of Labor Statistics has identified statisticians as a job trend that will grow faster than most occupations, with an expected growth rate of 33%. As a result of the increased opportunities, now is an excellent time for aspirants to upskill and dive deep into a successful career in some of the most popular industries.

**What Is Statistics, and Why Is It Important?**

Statistics is a branch of mathematics that deals with the study of data. Population data derived from machine learning, sample distributions, survey results, data analysis, normal distribution, hypothesis testing, research data, and more can all be found in data sets. You may enroll in online statistics courses for a variety of reasons. Aside from being extremely adaptable, there are a number of other benefits that I will outline below.

- Statistics courses online will give you a solid mathematical understanding of concepts and ideas that aren’t always put into practice.
- Your computer science skills will benefit from an online statistics course.
- By allowing you to use computer jargon, these courses will assist you in overcoming application obstacles.
- They’ll aid in developing your analytical, interpolation, data cleansing, and data-crunching abilities.

Statistics are important because they allow you to understand the characteristics of a small group in comparison to a larger population. You can learn about a large group of people using statistics without having to collect data from everyone.

**10 Free Online Courses in Statistics**

**1. Introduction to Statistics by Stanford University – Coursera**

This course is offered through Coursera. This online statistics course focuses on developing solid foundational statistical skills so that learners can effectively work with data and communicate insights. The central limit theorem, descriptive statistics, sampling and randomized controlled experiments, probability, sampling distributions, and the central limit theorem are all topics covered in this course. In addition, key concepts like regression, a common test of significance, and resampling are thoroughly discussed.

Learners will be able to perform data analysis, distinguish between descriptive and prescriptive statistical concepts, work with data sets, and select appropriate tests for various situations by the end of the course. Learners who want to pursue advanced statistical analysis and machine learning courses will find the concepts covered in this course sufficient.

**2. Advanced Statistics for Data Science Specialization by John Hopkins University – Coursera**

On Coursera, you can take a specialization course. The initial modules of the certification program cover fundamental concepts in probability and statistics. A mathematical statistics boot camp is also available, covering the concepts and methods used in biostatistical applications. In addition, the students will learn about advanced linear modeling concepts, data science modeling tools, least-square and linear regression, hypothesis testing, likelihood concepts, and distribution.

The students will then dive into the practical implementations of the models in order to perform multivariate regression using the R programming language. In addition, the online statistics course provides a solid foundation in linear algebraic requirements for data science and the mathematical perspective of linear statistical models.

**3. Statistics with R Specialization by Duke University – Coursera**

This is one of Coursera’s most popular statistics online courses. The course covers key data analysis and visualization concepts in R and how to create repeatable analytical reports. In addition, the students will learn how to make data-driven decisions using statistical inference, Bayesian statistical inference, and modeling. Furthermore, the students will learn how to effectively communicate statistical results and evaluate data-driven decisions and data wrangling with R packages for data analysis.

**4. Statistics with Python Specialization by University of Michigan – Coursera**

This course is offered through Coursera. The specialization is designed to teach learners how to use Python to learn various statistical concepts. Learners will comprehend data sources, types of data, and the data collection, analysis, and management process. Furthermore, the students will be familiar with data exploration and visualization techniques. Furthermore, the students will delve into data assessment theories, build confidence interval concepts, and interpret inferential results.

Furthermore, advanced statistical modeling procedures are thoroughly covered, with practical hands-on sessions to help students master the skills. Finally, the students will be able to connect research questions to statistical and data analysis methods in order to solve complex problems in a real-world setting.

**5. Data Science: Statistics and Machine Learning Specialization by Johns Hopkins University – Coursera**

This online statistics course is available through Coursera. The course is designed for students who have a basic understanding of statistics and machine learning. It covers statistical inference, regression models, and data analysis machine learning algorithms. Learners will also be able to create data products using a variety of tools and techniques, as well as work with real-world data. Furthermore, the students will have a thorough understanding of how to build and apply prediction functions and how to use advanced statistics to draw conclusions about populations and scientific insights from data.

**6. Probability and Statistics in Data Science using Python**

Probability and Statistics in Data Science with Python is one of the free online statistics courses with credentials. It’s a high-level online statistics course that covers probability and statistics fundamentals. This course will teach you both the mathematical theory and how to use Jupyter notebooks to apply it to real-world data.

**7. Learn how to Use Statistics Step-by-step**

This course is the foundation of statistics because if you understand it, you will be able to use it throughout the statistical analysis. This free online course on Udemy covers various statistical metrics, including central tendency, deviation measure, variance, standard deviation, moment, skewness, and kurtosis. On the other hand, these factors are used to comprehend a statistical variable fully and grasp various frequency distribution curves, their skewness, and kurtosis using simple explanations and examples. It also teaches students how to use the Lorenz curve and Ginni’s index to visualize and measure distribution inequalities.

**8. Intro to Statistics– Udacity**

This course for beginners is completely free and covers data visualization, probability, and many basic statistical concepts such as regression, hypothesis testing, etc. This online statistics course will teach you about data visualization and relationships, Bayes Rule and Correlation vs. Causation, Maximum Likelihood estimation, mean, median, and mode, statistical inference, and regression analysis.

**9. Introduction to Statistics– Coursera**

Auditing this course is completely free. That means you can get all of the course materials for free. Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions, and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, and Multiple Comparisons are all covered in this course. Overall, this course is useful for brushing up on the fundamentals. If you are a complete beginner, you should enroll. It is only necessary to have a basic understanding of computers and productivity software.

**10. Intro to Inferential Statistics– Udacity**

This is a free statistics course in its entirety. This course will teach you how to use sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and the chi-squared test to estimate population parameters. Professionals in the industry teach this course, and you will learn through various exercises. If you have a basic understanding of descriptive statistics, you should enroll.

**Why Should You Take An Online Statistics Course?**

In today’s world of constant innovation, industry competition is at an all-time high. On the other hand, millions of data are generated from various sources every day. Even though new technologies have aided in faster data analysis, statistics experts continue to be a valuable asset to various organizations. Deriving meaningful insights from data to generate business decisions to achieve business goals has become critical in the new era of data-driven decisions in all industries.

Data has become increasingly important in various fields, including weather forecasting, finance, insurance, sports, supply chain, manufacturing, government agencies, life sciences, and healthcare. Appropriate data analysis can provide significant knowledge about evaluating performance and developing future strategies to address challenges or gain a competitive advantage in the market. Furthermore, multidisciplinary fields like data science and big data have created a high demand for statisticians.

Upskilling with the appropriate theoretical and practical skills is required to build a career as a successful statistics professional in multi-disciplinary industries. As a result, it’s critical to choose an online statistics course that exposes students to all of the necessary skills and provides industry-recognized certificates to demonstrate their abilities. As a result, the article highlighted some of the most popular statistics courses available on the internet.

You should be familiar with statistics if you want to work in data science. That’s why I came up with the idea of sharing the 9 Best Free Online Statistics Courses with you. These free statistics courses will assist you in learning statistics. These statistics courses were gathered from a variety of sources. Furthermore, all of the courses are completely free. So, without further ado, let’s begin our search for the Best Free Online Statistics Courses.

**Other Free Online Courses for Statistics**

**1. Introduction to Descriptive Statistics – Udacity Duration**

This is Udacity’s third free statistics course. This course will teach you the fundamental terms and concepts of statistics. Also, learn how to calculate and interpret values such as Mean, Median, Mode, Sample, Population, and Standard Deviation. You’ll also learn how to use bar graphs, histograms, box plots, and other common visualizations to explore data. Also how to use distributions to make probabilistic data predictions. If you have a basic understanding of algebra and arithmetic, you should enroll.

**2. Bayesian Statistics: From Concept to Data Analysis– Coursera**

This is another online statistics course that is available for free to audit. The basics of probability and Bayes’ theorem are covered in this course. The book then goes over statistical inference from both a frequentist and a Bayesian standpoint. After that, you’ll learn how to choose prior distributions and create discrete data models. Finally, this course covers conjugate and objective Bayesian analysis for continuous data. If you have prior knowledge of basic statistics (for example, probability, the Central Limit Theorem, confidence intervals, and linear regression) and calculus, you should enroll.

**3. Introduction to Bayesian Statistics– Udemy**

This is a completely free statistics course. You will learn Bayesian statistics from the ground up in this course. You’ll also learn about conditional probability, subjective approaches to probability, and how to model probability problems using Venn and Tree diagrams.

**4. Python and Statistics for Financial Analysis– Coursera**

Coursera offers a free to audit course. This course combines python programming with statistical concepts. Visualizing and munging stock data, random variables and distribution, sampling and inference, and Linear Regression Models for Financial Analysis are all covered in this online statistics course. You’ll also create a model using multiple global market indices to forecast the price change of an S&P500 ETF. If you have a basic understanding of probability, you should enroll.

**5. Statistics literacy for non-statisticians– Udemy**

In this online statistics course, you will learn the fundamentals of statistics, such as p-value, ANOVA, variance, and so on. This is not an advanced-level course, and it does not cover the math of the analyses or the software used to conduct them. And you’ll only get a high-level overview of the most important statistical techniques. If you are unfamiliar with statistics but want to learn the basics, you should enroll.

**6. Statistics and probability– Khan Academy**

This course covers everything from fundamental probability and distributions to more advanced topics like inference and ANOVA models. After reading an Introductory Statistics book like Bayesian Statistics the Fun Way, which is more theoretical and has less code, this course is the best next step. The majority of Khan Academy courses include short, entertaining videos with quizzes. Points are awarded in quizzes. These quizzes will assist you in assessing your statistical knowledge. If you have a basic understanding of math, you should enroll.

## List of Free Online Courses on Statistics

Sr. No | Course Name | Ratings | Price | Course Content |

$1 | Math for Middle Schoolers: Statistics | 4.2/5 | Free | Understand the different statistics topics generally taught in middle school |

$2 | Why Numbers Matter – Online Course | 4.9/5 | Free | Learn how to use quantitative research to make difficult decisions and solve real-world problems. |

$3 | Time Series Analysis | 4.6/5 | Free | Time series models with real data examples using the R statistical software. |

$4 | Capstone Exam in Statistics and Data Science | 4.6/5 | FrThis culminating assessment includes probability | Probability, data analysis, statistics, and machine learning in this culminating assessment. |

$5 | Data Science: Probability | 3.9/5 | Free | Gain important foundational knowledge in probability theory essential for a data scientist. |

$6 | Fat Chance: Probability from the Ground Up | 4.6/5 | Free | A deeper understanding of probability and statistics. |

7 | I “Heart” Stats: Learning to Love Statistics | 4.6/5 | Free | Get to know stats, build a healthy bond, and maybe even fall in love! |

8 | Fundamentals of Statistics | 4.2/5 | Free | Understanding the principles underpinning statistical inference: estimation, hypothesis testing, and prediction. |

9 | Data Science: Linear Regression | 4.8/5 | Free | Learn how to use R to implement linear regression in data science. |

10 | Introduction to Probability – The Science of Uncertainty | 4.1/5 | Free | Probabilistic models, including random processes and the basic elements of statistical inference. |

11 | Introduction to Probability: Part 1 – The Fundamentals | 4/5 | Free | Part 1: Probabilistic models, including random processes and the basic elements of statistical inference. |

12 | Statistics: Unlocking the World of Data | 4.8/5 | Free | Explore the ideas and methods behind the statistics you encounter in everyday life. |

13 | Introductory Statistics: Sample Survey and Instruments for Statistical Inference | 4.9/5 | Free | This course aims to introduce basic concepts of sample surveys and to teach the statistical inference process using real-life examples. |

14 | Data Science: Inference and Modeling | 4.5/5 | Free | Learn inference and modeling: statistical tools in data analysis. |

15 | Computational Probability and Inference | 5/5 | Free | Learn the fundamentals of probabilistic analysis and inference. |

16 | Statistical Modeling and Regression Analysis | 4.2/5 | Free | linear regression models along with real data examples using the R. |

17 | Principles, Statistical and Computational Tools for Reproducible Science | 4.6/5 | Free | Learn skills and tools that support data science and reproducible research. |

18 | Probability: Distribution Models & Continuous Random Variables | 4.7/5 | Free | Including normal distribution, and continuous random variables to prepare for a career in information and data science. |

19 | Introduction to Probability | 4.5/5 | Free | Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty. |

20 | Introductory Statistics: Basic Ideas and Instruments for Statistical Inference | 4.1/5 | Free | This course utilizes real-life applications of Statistics in an exploration of the Statistical Inference process. |

21 | Probability – The Science of Uncertainty and Data | 4.1/5 | Free | With this introduction to probabilistic models, data science includes random processes and the basic elements of statistical inference. |

22 | Statistical Inference and Modeling for High-throughput Experiments | 4.6/5 | Free | A focus on the techniques commonly used to perform statistical inference on high throughput data. |

23 | Introduction to Probability: Part II? Inference & Processes | 4.4/5 | Free | Learn how to use probability theory to develop the basic elements of statistical inference and important random process models |

24 | Introductory Statistics: Analyzing Data Using Graphs and Statistics | 4.3/5 | Free | This course teaches basic statistical concepts and explores many compelling applications of statistical methods using real-life applications of Statistics. |

25 | Statistics for Business – II | 4.9/5 | Free | Examine data drawn from allied fields of business such as Finance and HR, and learn how to simulate data to follow a specified distribution. |

26 | Policy Analysis Using Interrupted Time Series | 4.7/5 | Free | A comprehensive course on conducting and presenting policy evaluations using interrupted time series analysis. |

27 | Probability: Basic Concepts & Discrete Random Variables | 4.7/5 | Free | Learn fundamental concepts of mathematical probability to prepare for a career in the growing field of information and data science. |

28 | Bayesian Statistics | 3.9/5 | Free | In which one’s inferences about parameters or hypotheses are updated as evidence accumulates. |

29 | Statistics for Marketing | 4.9/5 | Free | This course takes a deep dive into the statistical foundation upon which Marketing Analytics is built. |

30 | Introduction to Probability and Data with R | 4.7/5 | Free | This course introduces you to sampling and exploring data and basic probability theory and Bayes’ rule. |

31 | A Crash Course in Causality: Inferring Causal Effects from Observational Data | 4.8/5 | Free | We have all heard the phrase “correlation does not equal causation”. |

32 | An Intuitive Introduction to Probability | 4.7/5 | Free | This course will provide you with an intuitive and practical introduction to Probability Theory. |

33 | Mathematics for Machine Learning: PCA | 4/5 | Free | Mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. |

34 | Linear Regression and Modeling | 4.7/5 | Free | This course introduces simple and multiple linear regression models. |

35 | Business Applications of Hypothesis Testing and Confidence Interval Estimation | 4.8/5 | Free | Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. |

36 | Statistics with R Capstone | 4.7/5 | Free | The capstone project will be an analysis using R that answers a specific scientific/business question |

37 | Basic Statistics | 4.7/5 | Free | In this course you will learn the basics of statistics; not just how to calculate them. |

38 | Probability and Statistics: To p or not to p? | 4.8/5 | Free | Probability and Statistics: To p or not to p? from the University of London. We live in an uncertain and complex world. |

39 | Methods and Statistics in Social Science – Final Research Project | 3.9/5 | Free | The Final Research Project consists of a research study that you will perform in collaboration with fellow learners. |

40 | Mathematical Biostatistics Boot Camp 1 | 4.5/5 | Free | Probability and statistical concepts are used in elementary data analysis. It will be taught at an introductory level for students with junior. |

41 | Advanced Statistics for Data Science Specialization | 4.3/5 | Free | Familiarize yourself with fundamental concepts in probability and statistics, data analysis, and linear models for Data Science. |

42 | Bayesian Statistics: Techniques and Models | 4.8/5 | Free | This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. |

43 | Math behind Moneyball | 4.5/5 | Free | probability, math, and statistics can help baseball, football, and basketball teams improve, player and lineup selection, and game strategy. |

44 | Improving your statistical inferences | 4.9/5 | Free | Draw better statistical inferences from empirical research. interpret p-values, effect sizes |

45 | Inferential Statistics | 4.8/5 | Free | Statistical inference methods for numerical and categorical data. |

46 | Network Dynamics of Social Behavior | 4.5/5 | Free | Network Dynamics of Social Behavior from the University of Pennsylvania. |

47 | Introduction to Statistics | 4.5/5 | Free | This course is designed to explain the fundamental of statistics. The course contains four weeks or four modules. |

48 | Statistical Learning (Self-Paced) | 4.5/5 | Free | This is an introductory-level course in supervised learning, with a focus on regression and classification methods. |

49 | Introduction to Statistical Inference | 4.9/5 | Free | This course is about, statistical inference is the process of using data analysis to draw conclusions about a population or process beyond the existing data. |

50 | Statistical Reasoning | 4/5 | Free | It is designed for people who want to learn more about Statistics. |

51 | Statistics | 4.5/5 | Free | We live in a time of unprecedented access to information…data. |

52 | Intro to Statistics | 4.3/5 | Free | Techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics. |

53 | Intro to Inferential Statistics | 4.9/5 | Free | Inferential statistics allow us to draw conclusions from data that might not be immediately obvious. |

54 | Intro to Descriptive Statistics | 3.9/5 | Free | Descriptive statistics will teach you the basic concepts used to describe data. |

55 | Foundations of Data Science | 4.5/5 | Free | earn the fundamental concepts in probability, statistics, optimization, and linear algebra which form the foundations for data science. |

**Conclusion**

I hope that these 10 Best Free Online Statistics Courses will assist you in learning statistics for data science. My goal is to provide you with the best learning resources possible. Please feel free to ask me any questions or concerns in the comments section.

**Frequently Asked Questions**

**What is the definition of statistics?**Statistics is a method for gathering, organizing, analyzing, interpreting, and presenting data to solve a specific problem. It assists you in studying and solving critical problems by employing a variety of methods and data sets.

**What are the Best Free Statistics Courses Available on the Internet?**Some of the best free online statistics courses are listed below:

Math for Middle Schoolers: Statistics

Why Numbers Matter – Online Course

Time Series Analysis

Capstone Exam in Statistics and Data Science

Data Science: Probability

Fat Chance: Probability from the Ground Up

Fundamentals of Statistics

Data Science: Linear Regression

**What are the Career Options in Statistics?**In the field of statistics, you can work in the following positions:

Statistician

Data Analyst

Professor

Research Analyst

Biostatistician

Econometrician

Epidemiologist

Data Scientist

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