Global edtech, led by top experts

Free Statistics Courses

Statistics is a branch of mathematics employed as an essential tool across various industries. Great Learning Academy offers Statistics courses to inculcate expertise in you to work with Descriptive Statistics, Inferential Statistics, Machine Learning, and Data Science applications. You will gain skills to implement statistics techniques in data visualization, distribution functions, and analytics. Enroll in these online Statistics courses and gain course completion certificates. 

 

2.1L+ Learners
7 Courses
4.52 average rating
Avg course rating

Begin your learning journey

Key Highlights

certificate icon
Earn an industry-recognized certificate
flexible schedule icon
Start anytime, learn on your schedule
expert instructors icon
Taught by industry experts and top faculty

Begin your learning journey

Key Highlights

certificate icon
Earn an industry-recognized certificate
flexible schedule icon
Start anytime, learn on your schedule
expert instructors icon
Taught by industry experts and top faculty

Empowering millions through professional learning

Empowering millions through professional learning

  • star

    4.8

  • star

    4.89

  • star

    4.94

  • star

    4.7

  • star

    4.8

  • star

    4.89

  • star

    4.94

  • star

    4.7

All Statistics Courses

Filter by

Level
Time to complete

PRO & UNIVERSITY PROGRAMS

Boost your career by mastering in-demand skills through expert guidance, AI-powered learning, and hands-on projects.

  • Statistics for Data Science & Analytics

    Great Learning Academy

    Statistics for Data Science & Analytics

    40 coding exercises · 3 projects · 3.5 hours

    Skills: Statistics, Probability, Mean, Median, Standard Deviation

    Pro icon Pro

    View Course

    Statistics for Data Science & Analytics

    40 coding exercises · 3 projects · 3.5 hours

    What you’ll learn:

    • Introduction to Statistics
    • Probability
    • Descriptive Statistics

    View Course

  • Data Science and Machine Learning Program

    MIT IDSS

    Data Science and Machine Learning Program

    12 weeks · Online · Learn from MIT Faculty
    University icon University

    View Program

    Data Science and Machine Learning Program

    12 weeks · Online · Learn from MIT Faculty

    View Program

  • Free Statistics Courses

    Statistics for Data Science

    Great Learning Academy

    Statistics for Data Science

    star 4.58 · 68.7K+ learners · 7.5 hours

    Skills: Probability,Statistics,Normal Distribution,Sampling Distribution,Hypothesis,Central Limit Theorem

    Free icon Free

    View Course

    Statistics for Data Science

    star 4.58 · 68.7K+ learners · 7.5 hours

    What you’ll learn:

    • Basics of statistics
    • Descriptive statistics
    • Case study

    View Course

    Statistical Analysis

    Great Learning Academy

    Statistical Analysis

    star 4.5 · 18K+ learners · 1.0 hours

    Skills: Statistical Analysis, EDA

    Free icon Free

    View Course

    Statistical Analysis

    star 4.5 · 18K+ learners · 1.0 hours

    What you’ll learn:

    • Descriptive Statistical Analysis
    • Exploratory Analysis
    • Insurance Company Demo - Introduction

    View Course

    Statistical Methods for Decision Making

    Great Learning Academy

    Statistical Methods for Decision Making

    star 4.44 · 63.7K+ learners · 2.0 hours

    Skills: Hypothesis Testing,Type I and Type II error, Chi-Square test, ANOVA

    Free icon Free

    View Course

    Statistical Methods for Decision Making

    star 4.44 · 63.7K+ learners · 2.0 hours

    What you’ll learn:

    • Sampling
    • Normal Distribution
    • Hypothesis Testing

    View Course

    Importance of Statistics in Machine Learning

    Great Learning Academy

    Importance of Statistics in Machine Learning

    star 4.46 · 1.6K+ learners · 1.0 hours

    Skills: Big Data, Statistics and Measures of Central Tendency

    Free icon Free

    View Course

    Importance of Statistics in Machine Learning

    star 4.46 · 1.6K+ learners · 1.0 hours

    What you’ll learn:

    • Standard Deviation
    • Coefficient of Variation
    • Measures of Central Tendency

    View Course

    Statistics for Machine Learning

    Great Learning Academy

    Statistics for Machine Learning

    star 4.58 · 42.7K+ learners · 2.0 hours

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

    Free icon Free

    View Course

    Statistics for Machine Learning

    star 4.58 · 42.7K+ learners · 2.0 hours

    What you’ll learn:

    • Outline - Descriptive statistics
    • Data and Histogram
    • Central Tendency and 3 Ms

    View Course

    Inferential Statistics

    Great Learning Academy

    Inferential Statistics

    star 4.56 · 4.6K+ learners · 1.0 hours

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

    Free icon Free

    View Course

    Inferential Statistics

    star 4.56 · 4.6K+ learners · 1.0 hours

    What you’ll learn:

    • Introduction to Statistics
    • Data Collection for Statistics
    • Types of Statistical Analysis

    View Course

    Introduction to Descriptive Statistics

    Great Learning Academy

    Introduction to Descriptive Statistics

    star 4.46 · 9.7K+ learners · 1.0 hours

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

    Free icon Free

    View Course

    Introduction to Descriptive Statistics

    star 4.46 · 9.7K+ learners · 1.0 hours

    What you’ll learn:

    • Introduction to Statistics
    • What is Statistics?
    • Data Collection for Statistics

    View Course

    Statistics for Data Science

    Great Learning Academy

    Statistics for Data Science

    Skills: Probability,Statistics,Normal Distribution,Sampling Distribution,Hypothesis,Central Limit Theorem

    star 4.58 · 68.7K+ learners · 7.5 hours
    Free icon Free

    View Course

    Statistical Analysis

    Great Learning Academy

    Statistical Analysis

    Skills: Statistical Analysis, EDA

    star 4.5 · 18K+ learners · 1.0 hours
    Free icon Free

    View Course

    Statistical Methods for Decision Making

    Great Learning Academy

    Statistical Methods for Decision Making

    Skills: Hypothesis Testing,Type I and Type II error, Chi-Square test, ANOVA

    star 4.44 · 63.7K+ learners · 2.0 hours
    Free icon Free

    View Course

    Importance of Statistics in Machine Learning

    Great Learning Academy

    Importance of Statistics in Machine Learning

    Skills: Big Data, Statistics and Measures of Central Tendency

    star 4.46 · 1.6K+ learners · 1.0 hours
    Free icon Free

    View Course

    Statistics for Machine Learning

    Great Learning Academy

    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

    star 4.58 · 42.7K+ learners · 2.0 hours
    Free icon Free

    View Course

    Inferential Statistics

    Great Learning Academy

    Inferential Statistics

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

    star 4.56 · 4.6K+ learners · 1.0 hours
    Free icon Free

    View Course

    Introduction to Descriptive Statistics

    Great Learning Academy

    Introduction to Descriptive Statistics

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

    star 4.46 · 9.7K+ learners · 1.0 hours
    Free icon Free

    View Course

    Learner reviews of the Free Statistics Courses

    Our learners share their experiences of our courses

    4.52
    71%
    19%
    6%
    1%
    2%
    Reviewer Profile

    4.0

    “Simple to Understand and Easy to Resume After a Break”
    The course "Statistics for Data Science" is designed to be straightforward and accessible, ensuring that concepts are easy to grasp. The material is structured in a way that allows learners to pick up where they left off, even after taking a break, without losing continuity. The course is tailored for both beginners and those with some background in statistics, making complex topics understandable through clear explanations and practical examples. Whether you're stepping away for a short time or diving back in after a longer pause, you'll find the content intuitive and easy to re-engage with, enabling a smooth and effective learning experience.

    LinkedIn Profile

    Reviewer Profile

    5.0

    “I Had a Very Nice Experience with This Course”
    Very nice course. The instructor is awesome and explains the topics very well.

    LinkedIn Profile

    Reviewer Profile

    5.0

    “A Nicely Structured Course for Statistics for Data Science”
    I like the project assignment as well as the extensive quiz.

    LinkedIn Profile

    Reviewer Profile

    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.

    LinkedIn Profile

    Reviewer Profile

    5.0

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

    LinkedIn Profile

    Reviewer Profile

    5.0

    “Engaging Content, Practical Applications, and Insightful Discussions”
    I appreciate the course's engaging content, practical applications, and insightful discussions. It offers real-world examples and interactive activities that enhance understanding. The well-structured lessons and supportive community make learning enjoyable and effective.

    LinkedIn Profile

    Reviewer Profile

    5.0

    “I Learned Real-Life Statistical Business Applications from This Amazing Course”
    The best part of this course is the real-life examples from a business perspective. These examples made me learn the critical statistical methods easily. Loved the instructor's teaching style.

    LinkedIn Profile

    Reviewer Profile

    5.0

    “Teaching Techniques by the Professor”
    The teaching technique is very nice and impactful. I understand very well and was able to pass the quiz test with good marks. So, thank you.

    LinkedIn Profile

    Reviewer Profile

    4.0

    “I Enjoyed the Interactive Discussions and Practical Applications of the Concepts”
    What I liked most was how the course encouraged critical thinking and real-world problem-solving. The diverse perspectives from fellow participants enriched my understanding, and the hands-on activities made the material more engaging. Overall, it was a valuable learning experience that deepened my knowledge and skills in the subject.

    LinkedIn Profile

    Reviewer Profile

    5.0

    “In-Depth Explanation of the Statistics Part of ML”
    Easily graspable content with deep teachings. Each point was explained in a descriptive manner.

    LinkedIn Profile

    Learn Statistics For Free & Get Completion Certificates

    Statistics is the study of data collection, organization, analysis, interpretation, and presentation. It covers all data components, such as planning and data collecting, in terms of survey and experiment design.

     

    Statistics can infer relationships between variables, test hypotheses, and make predictions. It is widely used in many fields, such as business, economics, finance, and medicine. 

     

    The basic statistics concepts include probability and random variables, which define and measure the uncertainty associated with a given dataset. Probability theory describes the likelihood of specific outcomes and calculates the chances of future events. Random variables are used to describe the behavior of a system or process. 

     

    Statistical methods are used to analyze data and draw conclusions from it. Several types of statistical analysis can be used, such as descriptive statistics, inferential statistics, and multivariate statistics. Descriptive statistics summarize and describe data, while inferential statistics are used to make conclusions about a data population based on a sample. Multivariate statistics are used to analyze several variables at once. 

     

    Statistics also includes the use of software to analyze data. Commonly used statistical software packages, including SPSS, SAS, and R. These packages are used to analyze data and to create graphical representations of the data. They are also used to create predictive models and to perform hypothesis testing. It is an essential tool that can be used to help make decisions and to understand various aspects. It is used in a wide variety of fields to help make informed decisions.

     

    Statistics for Machine Learning is an essential concept for individuals who want to gain a better understanding of the data that is being collected and used in modern machine learning algorithms. Statistics are a key component of machine learning and enable the development of more accurate and reliable models. Understanding the underlying concepts of statistics will help individuals better understand the data being used and the results being produced. 

     

    The most important aspect of statistics for machine learning is the ability to identify meaningful patterns and relationships in the data. By understanding the principles of correlation and regression, individuals can look for significant relationships between the variables that are being studied. This is essential to understanding the data and the relationships between them, and it is also helpful in identifying potential areas of improvement in the data. 

     

    Another important concept in statistics for machine learning is probability. Probability is an essential concept in understanding how the data is used and how it can be used to make predictions. By understanding the concept of probability, individuals can better understand the data and make better predictions. 

     

    In addition to the concepts of correlation, regression, and probability, individuals should also be familiar with the concept of sampling. Sampling is an essential concept in understanding the accuracy of data. By sampling the data, individuals can better determine the accuracy of the data and make more informed decisions about the data.

     

    There are several types of statistical methods used in Data Science. These include descriptive statistics, inferential statistics, predictive analytics, and machine learning. 

     

    • Descriptive statistics summarize and describe the data, such as the mean, median, and mode. These techniques can be used to identify patterns and trends in the data. 
    • Inferential statistics are used to make predictions/assumptions based on the data. This can include using the data to make assumptions about the population or to draw conclusions about the data.
    • Predictive analytics is used to create models that predict the outcomes of future events. This can include identifying factors that may influence an outcome, such as customer behavior or market trends. 

     

    Some typical applications of statistics in Data Science include predictive analytics, market segmentation, forecasting, and optimization. Statistics can also identify potential risks and opportunities and make decisions based on the data.

     

    This subject page will provide you with a comprehensive list of free online statistics courses available for you to explore. Statistics courses are a great way to learn more about the subject, from basic concepts to advanced topics. Whether you're a beginner or an experienced statistician, these free online courses can help you gain a better understanding of the subject.

     

    You can further enhance your Data Science and Business Analytics skills through the Data Science certificate courses

    Meet your faculty

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

    instructor img

    Dr. P K Viswanathan

    Professor, Analytics & Operations
    instructor img

    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 are the prerequisites required to learn these Statistics courses?

    These courses include beginner-level Statistics introduction to give you foundational knowledge on required concepts. So, you need not do any homework before learning from these free courses. 
     

    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. 
     

    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. 
     

    Will I have lifetime access to these free Statistics courses with certificates?

    Yes. You will have lifetime access to these courses after enrolling in them and access to certificates after completing the course.
     

    Will I get a certificate after completing these free Statistics courses?

    Yes. After completing them successfully, you will receive a certificate of completion for each course. 
     

    How much do these Statistics courses cost?

    These are free courses, and you can enroll in them and learn for free online. 

    What are my next learning options after these Statistics courses?

    These free Statistics courses give you a competitive edge in your professional life. You can register for the Applied Data Science course to escalate your learning in Data Science and Business Analytics domains. 

     

    Is it worth learning Statistics?

    Mastering statistics enables you to select the best techniques for data collection, apply the right analysis, and effectively communicate the findings. Making judgments based on data, making predictions, and making scientific discoveries depend on statistics. 

     

    The important reasons to study statistics are to improve your ability to conduct research, read and analyze journal papers, behave as an informed consumer, and recognize when to employ outside statistical assistance.
     

    Why is Statistics so popular?

    Statistics provide you the ability to assess assertions supported by numerical data and assist you in differentiating between credible and doubtful findings. This feature is especially important now because individuals with various hidden motives offer so many data sources and interpretations.
     

    What jobs demand you learn Statistics?

    Jobs that are directly related to Statistics include,

    • Actuarial Analyst
    • Actuary
    • Civil Service Fast Streamer
    • Data Analyst
    • Data Scientist
    • Financial Risk Analyst
    • Investment Analyst
    • Market Researcher
    • Operational Researcher
    • Statistician
       

    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!

    Who are eligible to take these free Statistics courses?

    These courses have no prerequisites. Anybody can learn from these courses for free online. 

    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.