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  • Statistical Inference

Statistical Inference Courses

Statistical inference courses can help you learn hypothesis testing, confidence intervals, regression analysis, and data interpretation. You can build skills in analyzing sample data, making predictions, and drawing conclusions about larger populations. Many courses introduce tools like R and Python for statistical programming, as well as software for data visualization, which enhance your ability to apply statistical methods effectively.


More to explore:

Popular Statistical Inference Courses and Certifications


  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Statistical Inference

    Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Statistics, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics), Sample Size Determination, Data Analysis

    4.2
    Rating, 4.2 out of 5 stars
    ·
    4.5K reviews

    Mixed · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Data Science Foundations: Statistical Inference

    Skills you'll gain: Probability, Statistical Hypothesis Testing, Statistical Inference, Probability & Statistics, Probability Distribution, Statistical Methods, Statistics, Bayesian Statistics, Data Literacy, Sampling (Statistics), Applied Mathematics, Data Ethics, Data Analysis, Statistical Analysis, Quantitative Research, Data Science, Theoretical Computer Science, Sample Size Determination, Artificial Intelligence

    Build toward a degree

    4.4
    Rating, 4.4 out of 5 stars
    ·
    344 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    U

    University of Colorado Boulder

    Foundations of Probability and Statistics

    Skills you'll gain: Probability, Statistical Inference, Estimation, Probability & Statistics, Probability Distribution, Statistical Methods, Statistics, Markov Model, Bayesian Statistics, Data Literacy, Statistical Analysis, Sampling (Statistics), Applied Mathematics, Artificial Intelligence, Generative AI, Data Analysis, Data Science, Theoretical Computer Science, Machine Learning Algorithms, Mathematical Theory & Analysis

    Build toward a degree

    4.4
    Rating, 4.4 out of 5 stars
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    329 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Advanced Statistics for Data Science

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Regression Analysis, Bayesian Statistics, Statistical Analysis, Probability & Statistics, Statistical Inference, Statistical Methods, Statistical Modeling, Linear Algebra, Probability, Probability Distribution, R Programming, Biostatistics, Data Science, Statistics, Mathematical Modeling, Data Analysis, Data Modeling, Applied Mathematics

    4.4
    Rating, 4.4 out of 5 stars
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    783 reviews

    Advanced · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    D

    Duke University

    Inferential Statistics

    Skills you'll gain: Statistical Hypothesis Testing, Statistical Inference, Statistical Reporting, R (Software), Statistical Methods, Statistics, R Programming, Statistical Analysis, Probability & Statistics, Data Analysis, Sampling (Statistics), Probability Distribution, Software Installation

    4.8
    Rating, 4.8 out of 5 stars
    ·
    2.8K reviews

    Beginner · 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 Machine Learning, Statistical Programming, Regression Analysis, Data Visualization Software, Python Programming

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

    Beginner · Specialization · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    J

    Johns Hopkins University

    Data Science: Statistics and Machine Learning

    Skills you'll gain: Shiny (R Package), Rmarkdown, Model Evaluation, Regression Analysis, Exploratory Data Analysis, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Machine Learning Algorithms, Plotly, Interactive Data Visualization, Probability & Statistics, Statistical Machine Learning, Data Presentation, Data Visualization, Statistical Analysis, Statistical Modeling, R Programming, Machine Learning, GitHub

    4.4
    Rating, 4.4 out of 5 stars
    ·
    7.2K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Next level skills. New Year savings.

    Save on Coursera Plus
  • Status: Free Trial
    Free Trial
    I

    Imperial College London

    Statistical Analysis with R for Public Health

    Skills you'll gain: Logistic Regression, Analytical Skills, Correlation Analysis, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Statistics, Statistical Modeling, Biostatistics, Model Evaluation, Exploratory Data Analysis, Statistical Analysis, R (Software), Statistics, Statistical Methods, Data Wrangling, Public Health

    4.7
    Rating, 4.7 out of 5 stars
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    2.1K reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Preview
    Preview
    E

    Eindhoven University of Technology

    Improving your statistical inferences

    Skills you'll gain: Statistical Inference, Scientific Methods, Statistical Hypothesis Testing, Quantitative Research, Bayesian Statistics, Statistical Analysis, Probability & Statistics, Sample Size Determination, Research, R Programming, Data Sharing

    4.9
    Rating, 4.9 out of 5 stars
    ·
    799 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: Preview
    Preview
    S

    Stanford University

    Introduction to Statistics

    Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Probability Distribution

    4.6
    Rating, 4.6 out of 5 stars
    ·
    4.3K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Statistical Analysis Fundamentals using Excel

    Skills you'll gain: Data Visualization, Descriptive Statistics, Regression Analysis, Forecasting, Business Analytics, Data Analysis, Statistical Analysis, Statistical Methods, Microsoft Excel, Statistics, Spreadsheet Software, Predictive Analytics, Probability

    4.6
    Rating, 4.6 out of 5 stars
    ·
    134 reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    U

    University of Pittsburgh

    Statistics and Calculus Methods for Data Analysis

    Skills you'll gain: Statistical Analysis, Statistical Modeling, Predictive Modeling, Data Analysis, Statistics, Integral Calculus, Probability & Statistics, Data Science, Probability Distribution, Calculus, Mathematics and Mathematical Modeling, Applied Mathematics, Derivatives, Mathematical Modeling, Machine Learning, Algorithms

    Build toward a degree

    Beginner · Course · 1 - 4 Weeks

What brings you to Coursera today?

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In summary, here are 10 of our most popular statistical inference courses

  • Statistical Inference: Johns Hopkins University
  • Data Science Foundations: Statistical Inference: University of Colorado Boulder
  • Foundations of Probability and Statistics: University of Colorado Boulder
  • Advanced Statistics for Data Science: Johns Hopkins University
  • Inferential Statistics: Duke University
  • Statistics with Python: University of Michigan
  • Data Science: Statistics and Machine Learning: Johns Hopkins University
  • Statistical Analysis with R for Public Health: Imperial College London
  • Improving your statistical inferences: Eindhoven University of Technology
  • Introduction to Statistics: Stanford University

Skills you can learn in Probability And Statistics

R Programming (19)
Linear Regression (12)
Statistical Analysis (12)
Regression Analysis (10)
Biostatistics (9)
Bayesian (7)
Logistic Regression (7)
Probability Distribution (7)
Bayesian Statistics (6)
Medical Statistics (6)

Frequently Asked Questions about Statistical Inference

Statistical inference is a branch of statistics that allows us to make conclusions about a population based on a sample of data. It involves using data analysis to estimate population parameters, test hypotheses, and make predictions. Understanding statistical inference is crucial because it provides the tools to make informed decisions in various fields, including business, healthcare, and social sciences. By applying statistical inference, organizations can identify trends, assess risks, and improve outcomes based on data-driven insights.‎

Careers in statistical inference span a wide range of industries. Positions such as data analyst, statistician, biostatistician, and quantitative researcher often require strong skills in statistical inference. Additionally, roles in market research, quality assurance, and financial analysis also benefit from expertise in this area. As businesses increasingly rely on data to drive decisions, the demand for professionals skilled in statistical inference continues to grow.‎

To effectively learn statistical inference, you should focus on several key skills. First, a solid understanding of probability theory is essential, as it forms the foundation of statistical inference. Familiarity with statistical software, such as R or Python, is also important for data analysis. Additionally, skills in data visualization and interpretation will help you communicate your findings effectively. Lastly, critical thinking and problem-solving abilities are crucial for applying statistical methods to real-world scenarios.‎

There are several excellent online courses available for learning statistical inference. For a comprehensive introduction, consider the Statistical Inference course, which covers fundamental concepts and techniques. For those interested in data science applications, the Data Science Foundations: Statistical Inference Specialization offers a deeper dive into practical applications. Additionally, the Statistical Inference and Hypothesis Testing in Data Science Applications course focuses on hypothesis testing, a critical aspect of statistical inference.‎

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

  1. Preview the first module of many statistical inference 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 statistical inference, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn statistical inference effectively, start by selecting a course that matches your current knowledge level. Begin with foundational concepts, such as probability and descriptive statistics, before progressing to more advanced topics like hypothesis testing and confidence intervals. Engage with practical exercises and real-world datasets to apply what you learn. Additionally, participating in online forums or study groups can enhance your understanding and provide support as you navigate the material.‎

Typical topics covered in statistical inference courses include probability distributions, sampling methods, estimation techniques, hypothesis testing, confidence intervals, and regression analysis. Some courses may also explore advanced topics like Bayesian inference and statistical modeling. By covering these areas, learners gain a comprehensive understanding of how to analyze data and draw meaningful conclusions.‎

For training and upskilling employees in statistical inference, courses like the SAS Statistical Business Analyst Professional Certificate provide a structured approach to learning essential skills. Additionally, the Statistical Analysis with R for Public Health Specialization is ideal for those in the healthcare sector. These programs equip professionals with the necessary tools to apply statistical inference in their respective fields effectively.‎

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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