9 Best Online Statistics Courses in 2022 (Ranked and Reviewed)

This is our review of the best online statistics course in 2022.

Statistics is a critical field of study for data-driven decision-making.

In this post, we will rank and review the best online Statistics courses on the market. We will look at popular learning platforms such as edX, DataCamp, Udemy, and Udacity to find the highest quality courses that fit into your busy schedule.

We’ll also take a look at feathers, student enrollment numbers, pricing, and reviews to help you make an informed decision about which course is right for you!

We found that Introduction to Statistics in R (DataCamp) is the best overall statistics course in 2022. 

Every course we suggested has pros and cons. You can choose according to your study style. 

Let’s get started!

<Our Pick>
#1. Introduction to Statistics in R (DataCamp)

Best for Advanced: #2. Fundamentals of Statistics (MIT) 

Best for Intermediate: #3. Statistics and R (Harvard University)

Best for Budget: #4. Intro to Statistics (Udacity)

What are the best online statistics courses?

Here is my ranked and reviewed list of the best statistics online courses in 2022.

1. Introduction to Statistics in R (DataCamp)

Overview

This course is for beginners enrolled in 36,810 students. 

In this course, you’ll learn how to answer questions like those tested when you’re learning to collect and analyze sales data, practice calculating averages, using scatterplots to display the relationship between numeric values, and calculating the correlation.

You’ll also study probability, the foundation of statistical reasoning, and how to create well-designed research to come to your own conclusions based on data.

The topics covered are:

– Summary Statistics

– Random Numbers and Probability

– More Distributions and the Central Limit Theorem

– Correlation and Experimental Design

Top Features

• This course may be completed in 4 hours by watching 15 videos. 
• Self-paced learning. 
• It provides 45 practical exercises.
• This course is part of the following courses: Data Analyst with R, Data Scientist with R, Statistician with R, Statistics Fundamentals with R
•  There’s a transcript of the video.

Pricing

Your first class is free, but if you opt to go with the second one, you need to buy a subscription.

DataCamp offers a monthly subscription for $35 per month. You can learn all the lessons for $35 per month with your subscription. If you pay an annual subscription of $12.42 monthly, it’ll cost you $149 per year.

2. Fundamentals of Statistics (MIT) 

Overview

This is an advanced statistics course for those who have college-level of mathematic knowledge such as probability, single and multivariable calculus, vectors, and matrics. 

This course focuses on the continuous arithmetic building of countable sequence calculus algorithms and various forward approximation techniques and the design and analysis of asymptotic statistics.

This course will help you develop a deeper understanding of statistics by allowing you to go beyond simply learning a list of methods and the mathematical concepts that bind them together.

Top Feathers

• The expected length of time is 17 weeks approximately consuming 10-14 hours per week.
• It is an instructor-paced course which means instructor-led on the course schedule. 
• 130,491 students already enrolled.
• The video transcript is available. 
• There are some associate programs:
  • MicroMasters@Program in Statistics and Data Science
  • MicroMasters® Program in Statistics and Data Science (General track)

Pricing

3. Statistics and R (Harvard University)

Overview

This course teaches you how to use the R programming language to analyze statistical data. As an intermediate level, the course is for people who want to work with data in the life sciences.

You will learn how to understand and compute p-values and confidence intervals while analyzing data with R code. 

This course gives R programming examples in a way that helps you see how concepts and implementation are connected.

By using visualization capabilities, you will analyze data sets to understand the best approach to solve a given problem. 

This course also explains robust statistical techniques as alternatives when data do not satisfy the assumptions required by the standard approaches.

The topics covered are:

– Random variables

– Distributions

– Inference: p-values and confidence intervals

– Exploratory Data Analysis

– Non-parametric statistics

Top Features

• The estimated period is 4 weeks while consuming 2-4 hours per week.
• This course is self-paced learning. 
• This course needs some prerequisite skills: Basic programming, Basic math
• The video transcript will be provided. 
• This course is a part of the Professional Certificate in Data Analysis for Life Sciences

Pricing

4. Intro to Statistics (Udacity)

Overview

This course teaches you techniques for analyzing numerical data and the mathematical approaches for understanding complicated relationships.

As a beginner course for statistics, this course engages basic familiarity with algebra, including counting the mean, median, and mode of a given set of numbers.

The topics covered are:

– Visualizing relationships in data

– Probability

– Estimation

– Outliers and Normal Distribution

– Inference

– Regression

Top Features

• The estimated time to complete this course is approx 2 months.
• It is self-paced learning. 
• The course instructor Sebastian Thrun is an adjunct professor at Stanford University and an industry professional.
• It provides interactive quizzes. 
• You can watch the trailer for this course.

Pricing

This course is Free!

5. Statistics Fundamentals (Brilliant)

Overview

This course is for those with no prior statistical knowledge but it required some preunderstanding of probability and algebra. Brilliant provides the prerequisite courses: Introduction to Probability, Algebra through Puzzles. 

This course will teach you the theory and formulas of the median, mode, range, variance, and standard deviation. With them, you will be able to analyze graphs more precisely, determine your significance with regard to numerical values, and logically make the right decision.

By the end of this course, you’ll be able to calculate mathematical predictions, conduct experiments on data sets so that you can draw conclusions from them, and know the nature of data in a data set.

The topics covered are:

– Statistics Introduction

– Building Blocks

– Lying with Statistics

– Variance and Normal Curves

– Experiments

Top Features

• This course provides 26 interactive quizzes and more than 245 concepts and exercises. 
• You can learn this course self-paced allowing you to study on your own schedule.
• If your response is inaccurate, you can check a separate explanation of the correct answer.

Pricing

Brilliant offers a Free account and a Premium experience. The free account provides you to access the Today tab, where you can preview online classes and sync your progress between web and mobile devices.

Premium experience provides unrestricted access to more than 60 lectures, the offline mode in the mobile app, and the courses.

There are three options in the Premium experience: Monthly, Annual, Groups of 3+

When you subscribe to the Annual option, you pay $185.88 per year (15.49 per month)

6. Introduction to Statistics for Data Science using Python (IBM)

Overview

This course provides basic knowledge of statistical methods and procedures for beginners. It is offered by IBM in edX. 

You will learn how to collect data, summarize data using statistics, understand probability distribution, display and visualize data, understand relationships between data, make predictions, and analyze variance.

Once you understand the basics of statistics, you will work on a project with real data to understand different approaches to data types, appropriate evaluations of methods, and training to analyze and output data.

The topics covered are:

– Estimate and apply the measures of central tendency and the measures of dispersion to grouped and ungrouped data.

– Presenting, summarizing, and visualizing data in a way that is clear, concise, and useful for non-statisticians requires an overview.

– Use hypotheses to make appropriate data sets accessible.

– Operate hypothesis tests, correlation tests, and regression analysis.

– Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.

Top Features

• The estimated time to complete is 4 weeks spending 3-4 hours per week. 
• It is self-paced which means you will progress at your own pace.
• There is no prerequisite for this course.
• The video transcript is available. 

Pricing

7. Intro to Descriptive Statistics (Udacity)

Overview

Descriptive statistics is a course that will teach you how to describe data. 

This is a great course for beginners who want to learn about data science, economics, psychology, machine learning, and sports analytics.

This course assumes that you are familiar with fundamental algebra and arithmetic.

The topics covered are:

– Intro to Research Methods

– Visualizing Data

– Central Tendency

– Variability

– Standardizing

– Normal Distribution

Top Features

• Approximately 2 months are required to complete this course. 
• This course is self-paced learning. 
• This course will teach you the fundamentals of statistics, as well as walk you through basic probability.
• It provides you with interactive quizzes.
• You will learn from industry professionals. 

Pricing

This course is Free!

8. Intro to Inferential Statistics (Udacity)

Overview

Inferential statistics allow us to base our decisions on information that cannot be readily seen.

This course teaches you the analytical skills required to develop and validate hypotheses. You will learn how to perform statistical analyses such as t-tests, ANOVA tests, and regression using the binomial test, as well as understand and interpret comparisons as contained in their results.

This is a beginner course that requires you to understand descriptive statistics. 

The topics covered are:

– Estimation

– Hypothesis testing

– T-testing

– ANOVA

– Correlation

– Regression

– Chi-squared Tests

Top Feathers

• This course needs about 2 months to complete. 
• Self-paced learning
• This course uses Google Spreadsheets as a tool.
• By doing exercises, you will learn this course.
• This course will be taught by industry experts. 

Pricing

This course is Free!

9. Beginner Statistics for Data Analytics – Learn the Easy Way! (Udemy)

Overview

This is a beginner statistics course designed to teach you the essential statistical tools you’ll need for your analysis. In this course, 6,615 students enrolled. 

You’ll learn simple methods for applying advanced statistical analysis data in a short time. You will also learn visualization methods such as histograms and scatterplots to make your data easier to view.

You will be able to analyze your data effectively by applying basic statistical techniques for your past data.

The topics covered are:

– Introduction

– The Fundamentals of Statistics

– A deeper dive into Descriptive Statistics

– Inferential Statistics $ Estimates

– Intro to Regression analysis

– Creating and understanding a Regression

– Conclusion

Top Features

• Background in statistics is not necessarily required.
• This course is the highest-rated course on Udemy: 4.7 (1,800 ratings)
• It provides 3 hours of on-demand video and 3 downable resources. 
• There is a certificate of completion when you accomplish the course requirements.
• Top companies such as Nasdaq, Box, and Eventbrite offer this course to their employees.

Pricing

What is Statistics?

Statistics is the science of learning from data and making decisions under uncertainty. It applies to a wide range of problems, from detecting fraudulent activity to finding new cures for diseases. While statistics can be used to make predictions about the future, it is also often used to understand the past. For example, historians use statistics to study patterns of population change over time.

Despite its name, statistics is not about numbers. In fact, statistical analysis often starts with qualitative data, such as surveys or interviews. This data is then converted into numerical form so that it can be analyzed using statistical methods. The goal of statistical analysis is to draw conclusions from data that are as objective and precise as possible.

There are many different types of statistical analysis, but all share the same basic steps: collect data, summarize the data, and draw conclusions from the data. The Best online Statistics course will teach you how to carry out these steps in order to answer real-world questions. With a strong foundation in statistics, you’ll be able to tackle any problem that comes your way.

FAQ

Is it hard to learn statistics online?

Statistics is a branch of mathematics that is used to analyze, organize, and present data. Because statistics is such a broad field, it can be difficult to know where to start when learning it online. 

The best way to learn statistics is to find a course that covers the topics you are interested in and that is tailored to your level of understanding. 

There are many different online courses available, so you should be able to find one that meets your needs. 

Once you have found a course, it is important to follow the instructions carefully and to practice regularly. With effort and perseverance, you should be able to learn statistics online.

How long does it take to learn statistics?

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In many ways, it is similar to other mathematical disciplines, such as calculus and linear algebra. 

However, statistics also has its own unique set of concepts and principles. As a result, it can take some time to master the subject. 

In general, students who have a strong background in mathematics can expect to spend about two years studying statistics at the undergraduate level. 

Those who do not have a strong foundation in mathematics may need to spend an additional year or two acquiring the necessary skills. However, the length of time required to learn statistics ultimately depends on the individual student’s abilities and goals.

Is a degree in statistics worth it?

A degree in statistics can open up a lot of doors. Many businesses rely on statisticians to help make sense of data and to make sound decisions based on that data.

Statisticians are also in high demand in the government and in academia. A degree in statistics can lead to a very successful and financially rewarding career. 

However, it is important to note that a degree in statistics is not always necessary to work as a statistician. Many employers are willing to hire candidates with strong math skills and relevant experience, even if they do not have a formal education in statistics. 

As with any decision regarding education, it is important to carefully consider your goals and objectives before deciding whether or not to pursue a degree in statistics.

Conclusion

So, which online Statistics course should you choose? Our pick of the Best Online Statistics Courses is Introduction to Statistics in R (DataCamp). We highly recommend this course if you are looking for a comprehensive and well-rounded introduction to the topic.

Whichever route you decide to take, we hope that this ranking has helped make your decision easier and that you have now gained the skills needed to tackle Statistics confidently. Feathers down!

Here are some suggested articles for you to read: 9 Benefits Of Learning Python Language, 13 Best Python Courses (Ranked And Reviewed), and What Is Data Science?