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Statistics Courses

Statistics courses can help you learn data analysis, probability theory, hypothesis testing, and regression techniques. You can build skills in interpreting data sets, making informed predictions, and conducting surveys. Many courses introduce tools like R, Python, and Excel, that support performing statistical analyses and visualizing results. You'll also explore key topics such as descriptive statistics, inferential statistics, and experimental design, equipping you with the knowledge to tackle real-world data challenges.


Popular Statistics Courses and Certifications


  • Status: Free Trial
    Free Trial
    G

    Google

    The Power of Statistics

    Skills you'll gain: Sampling (Statistics), Descriptive Statistics, Statistical Hypothesis Testing, Data Analysis, Probability Distribution, Statistics, Data Science, Statistical Analysis, A/B Testing, Statistical Methods, Probability, Statistical Inference, Statistical Programming, Python Programming, Technical Communication

    4.8
    Rating, 4.8 out of 5 stars
    ·
    863 reviews

    Advanced · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Statistics with Python

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Modeling, Statistical Methods, Statistical Inference, Bayesian Statistics, Data Visualization, Statistics, Matplotlib, Statistical Visualization, Statistical Software, Probability & Statistics, Model Evaluation, Statistical Analysis, Jupyter, Statistical Programming, Statistical Machine Learning, Regression Analysis, Data Visualization Software, Python Programming

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.3K reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    G

    Google

    Google Advanced Data Analytics

    Skills you'll gain: Data Storytelling, Data Visualization, Data Ethics, Exploratory Data Analysis, Sampling (Statistics), Data Visualization Software, Feature Engineering, Regression Analysis, Descriptive Statistics, Logistic Regression, Statistical Hypothesis Testing, Model Evaluation, Data Analysis, Tableau Software, Data Science, Statistical Analysis, Machine Learning, Object Oriented Programming (OOP), Interviewing Skills, Python Programming

    Build toward a degree

    4.7
    Rating, 4.7 out of 5 stars
    ·
    11K reviews

    Advanced · Professional Certificate · 3 - 6 Months

  • Status: Preview
    Preview
    T

    The Hong Kong University of Science and Technology

    Python and Statistics for Financial Analysis

    Skills you'll gain: Statistical Inference, Pandas (Python Package), Probability & Statistics, Risk Analysis, Financial Trading, Financial Data, Data Manipulation, Statistical Analysis, Regression Analysis, Financial Analysis, Jupyter, Financial Modeling, Python Programming, Model Evaluation, Data Visualization, Data Import/Export

    4.4
    Rating, 4.4 out of 5 stars
    ·
    4.6K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    Data Analysis with Python

    Skills you'll gain: Exploratory Data Analysis, Model Evaluation, Data Transformation, Data Analysis, Data Cleansing, Data Manipulation, Data Import/Export, Predictive Modeling, Data Preprocessing, Regression Analysis, Data Science, Statistical Analysis, Pandas (Python Package), Scikit Learn (Machine Learning Library), Data-Driven Decision-Making, Matplotlib, Data Visualization, NumPy, Python Programming

    4.7
    Rating, 4.7 out of 5 stars
    ·
    20K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    IBM Machine Learning

    Skills you'll gain: Autoencoders, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Feature Engineering, Generative AI, Classification Algorithms, Regression Analysis, Dimensionality Reduction, Time Series Analysis and Forecasting, Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Reinforcement Learning, Generative Adversarial Networks (GANs), Artificial Intelligence and Machine Learning (AI/ML), Data Cleansing, Deep Learning, Data Science, Machine Learning, Python Programming

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    3.5K reviews

    Intermediate · Professional Certificate · 3 - 6 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Mathematics for Machine Learning

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, NumPy, Regression Analysis, Calculus, Applied Mathematics, Data Preprocessing, Unsupervised Learning, Feature Engineering, Machine Learning Algorithms, Jupyter, Advanced Mathematics, Statistics, Artificial Neural Networks, Algorithms, Mathematical Modeling, Python Programming, Derivatives

    4.6
    Rating, 4.6 out of 5 stars
    ·
    15K reviews

    Beginner · Specialization · 3 - 6 Months

  • Unlimited growth. Unbeatable savings.

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  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Genomic Data Science

    Skills you'll gain: Bioinformatics, Unix Commands, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Unix, Data Science, Data Management, Statistical Methods, Command-Line Interface, Statistical Hypothesis Testing, Linux Commands, Data Analysis Software, Data Quality, Data Structures, Data Analysis, Computer Science, Molecular Biology, R Programming, Python Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    6.8K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    P

    Packt

    Data Science Essentials: Analysis, Statistics, and ML

    Skills you'll gain: Plotly, Model Evaluation, NumPy, Plot (Graphics), Dashboard, Statistics, Pandas (Python Package), Data Analysis, Statistical Analysis, Regression Analysis, Data Manipulation, Python Programming, Analytics, Probability & Statistics, Statistical Methods, Applied Machine Learning, Probability, Data Science, Statistical Modeling, Performance Tuning

    4.7
    Rating, 4.7 out of 5 stars
    ·
    24 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of California San Diego

    Introduction to Discrete Mathematics for Computer Science

    Skills you'll gain: Graph Theory, Logical Reasoning, Combinatorics, Computational Logic, Deductive Reasoning, Cryptography, Probability, Computational Thinking, Encryption, Probability Distribution, Network Analysis, Public Key Cryptography Standards (PKCS), Theoretical Computer Science, Bayesian Statistics, Python Programming, Data Structures, Cybersecurity, Algorithms, Arithmetic, Visualization (Computer Graphics)

    4.5
    Rating, 4.5 out of 5 stars
    ·
    3.7K reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    U

    University of Michigan

    Sports Performance Analytics

    Skills you'll gain: Logistic Regression, Forecasting, Regression Analysis, Data Cleansing, Scikit Learn (Machine Learning Library), Supervised Learning, Data Processing, Sports Medicine, Correlation Analysis, Data Preprocessing, Predictive Modeling, Matplotlib, Applied Machine Learning, Statistical Modeling, Injury Prevention, Athletic Training, Analytics, Data Analysis, Statistical Analysis, Python Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    275 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    I

    IBM

    IBM Data Analyst

    Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Dashboard, Data Visualization Software, Plotly, Data Visualization, Data Presentation, Interactive Data Visualization, Generative AI, Model Evaluation, SQL, Data Transformation, Data Analysis, Statistical Visualization, IBM Cognos Analytics, Excel Formulas, Professional Networking, Data Import/Export, Microsoft Excel, Python Programming

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    97K reviews

    Beginner · Professional Certificate · 3 - 6 Months

What brings you to Coursera today?

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In summary, here are 10 of our most popular statistics courses

  • The Power of Statistics: Google
  • Statistics with Python: University of Michigan
  • Google Advanced Data Analytics: Google
  • Python and Statistics for Financial Analysis: The Hong Kong University of Science and Technology
  • Data Analysis with Python: IBM
  • IBM Machine Learning: IBM
  • Mathematics for Machine Learning: Imperial College London
  • Genomic Data Science: Johns Hopkins University
  • Data Science Essentials: Analysis, Statistics, and ML: Packt
  • Introduction to Discrete Mathematics for Computer Science: University of California San Diego

Frequently Asked Questions about Statistics

Statistics is the branch of mathematics that deals with collecting, analyzing, interpreting, presenting, and organizing data. It is crucial because it provides the tools and methodologies to make informed decisions based on data. In an increasingly data-driven world, understanding statistics allows individuals and organizations to identify trends, make predictions, and validate hypotheses. Whether in business, healthcare, social sciences, or technology, statistics plays a vital role in guiding strategies and improving outcomes.‎

A background in statistics can open doors to various career opportunities. Jobs in this field include data analyst, statistician, biostatistician, market researcher, and quantitative analyst. These roles often require the ability to interpret complex data sets and communicate findings effectively. Additionally, industries such as finance, healthcare, and technology are increasingly seeking professionals skilled in statistics to help drive decision-making processes and improve operational efficiency.‎

To pursue a career in statistics, you should develop a range of skills. Key competencies include proficiency in statistical software (like R or Python), a solid understanding of probability theory, data visualization techniques, and the ability to interpret and communicate statistical results. Additionally, critical thinking and problem-solving skills are essential, as they enable you to approach data analysis with a strategic mindset. Familiarity with data collection methods and experimental design is also beneficial.‎

There are many excellent online statistics courses available that cater to different levels of expertise. For beginners, the Foundations of Probability and Statistics Specialization offers a solid introduction. For those looking to apply statistics in data science, the Data Science: Statistics and Machine Learning Specialization is highly recommended. Additionally, the Business Statistics and Analysis Specialization provides practical skills for applying statistics in a business context.‎

Yes. You can start learning statistics on Coursera for free in two ways:

  1. Preview the first module of many statistics courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in statistics, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

Learning statistics can be approached through various methods. Start by identifying your learning style‚Äîwhether you prefer structured courses, hands-on projects, or self-study. Online platforms like Coursera provide a range of courses that cater to different preferences. Engage with practical exercises to apply what you learn, and consider joining study groups or forums to discuss concepts with peers. Regular practice and real-world application will reinforce your understanding and build confidence.‎

Typical topics covered in statistics courses include descriptive statistics, probability theory, inferential statistics, hypothesis testing, regression analysis, and data visualization. More advanced courses may explore Bayesian statistics, multivariate analysis, and statistical modeling. These topics provide a comprehensive foundation for understanding how to analyze and interpret data effectively, which is essential for making informed decisions in various fields.‎

For training and upskilling employees, courses like the Statistics and Applied Data Analysis Specialization are particularly beneficial. This specialization focuses on practical applications of statistics in real-world scenarios. Additionally, the Business Statistics and Analysis Specialization equips learners with essential skills for data-driven decision-making in business contexts. These courses can enhance workforce capabilities and drive organizational success.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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