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    • Statistical Modeling

    Statistical Modeling Courses Online

    Master statistical modeling for predictive analytics. Learn to build and validate models for various types of data.

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    Explore the Statistical Modeling Course Catalog

    • U

      University of Colorado Boulder

      Statistical Modeling for Data Science Applications

      Skills you'll gain: General Statistics, Probability & Statistics, Regression, Statistical Analysis, Experiment, Statistical Tests, Calculus, Linear Algebra, R Programming

      Build toward a degree

      4.2
      Rating, 4.2 out of 5 stars
      ·
      51 reviews

      Intermediate · Specialization · 3 - 6 Months

    • D

      Duke University

      Linear Regression and Modeling

      Skills you'll gain: Data Analysis, General Statistics, Probability & Statistics, Regression, Statistical Analysis, Statistical Tests, Correlation And Dependence, Plot (Graphics), Problem Solving, R Programming

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.7K reviews

      Beginner · Course · 1 - 4 Weeks

    • J

      Johns Hopkins University

      Data Science: Statistics and Machine Learning

      Skills you'll gain: R Programming, Data Analysis, Statistical Analysis, Statistical Programming, Probability & Statistics, Statistical Machine Learning, General Statistics, Machine Learning Algorithms, Human Learning, Machine Learning, Problem Solving, Statistical Tests, Regression, Computer Programming, Algorithms, Applied Machine Learning, Correlation And Dependence, Data Model, Estimation, Probability Distribution, Machine Learning Software, Natural Language Processing, Basic Descriptive Statistics, Exploratory Data Analysis, Data Visualization, Data Visualization Software, Interactive Design, Plot (Graphics), Product Development, Web Development

      4.4
      Rating, 4.4 out of 5 stars
      ·
      7.1K reviews

      Intermediate · Specialization · 3 - 6 Months

    • U

      University of Michigan

      Statistics with Python

      Skills you'll gain: General Statistics, Probability & Statistics, Statistical Analysis, Data Analysis, Statistical Tests, Basic Descriptive Statistics, Probability Distribution, Python Programming, Statistical Visualization, Statistical Programming, Plot (Graphics), Regression, Correlation And Dependence, Critical Thinking, Data Visualization, Exploratory Data Analysis, Computer Programming, Data Model, Bayesian Statistics

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.2K reviews

      Beginner · Specialization · 1 - 3 Months

    • U

      University of Michigan

      Fitting Statistical Models to Data with Python

      Skills you'll gain: General Statistics, Probability & Statistics, Regression, Statistical Analysis, Statistical Tests, Correlation And Dependence, Data Analysis, Plot (Graphics), Statistical Visualization, Data Model, Bayesian Statistics

      4.4
      Rating, 4.4 out of 5 stars
      ·
      692 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      E

      Eindhoven University of Technology

      Improving your statistical inferences

      Skills you'll gain: Probability & Statistics, Statistical Tests, General Statistics, R Programming, Statistical Programming, Bayesian Statistics

      4.9
      Rating, 4.9 out of 5 stars
      ·
      789 reviews

      Intermediate · Course · 1 - 3 Months

    • Placeholder
      M

      Meta

      Statistics Foundations

      Skills you'll gain: Basic Descriptive Statistics, Business Analysis, Data Analysis, Experiment, General Statistics, Marketing, Probability & Statistics, Probability Distribution, Regression, Statistical Analysis, Statistical Tests

      4.7
      Rating, 4.7 out of 5 stars
      ·
      281 reviews

      Beginner · Course · 1 - 3 Months

    • Placeholder
      U

      University of California, Irvine

      Predictive Modeling, Model Fitting, and Regression Analysis

      Skills you'll gain: Data Analysis, Data Model, General Statistics, Machine Learning, Probability & Statistics, Regression, Statistical Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      62 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Placeholder
      U

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      Skills you'll gain: Probability & Statistics, Bayesian Statistics, Probability Distribution, R Programming, Statistical Programming, Regression, General Statistics, Machine Learning

      4.8
      Rating, 4.8 out of 5 stars
      ·
      486 reviews

      Intermediate · Course · 1 - 3 Months

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      C

      Coursera Project Network

      Building Statistical Models in R: Linear Regression

      Skills you'll gain: R Programming, Statistical Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      17 reviews

      Beginner · Guided Project · Less Than 2 Hours

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      D

      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, General Statistics, Probability & Statistics, Statistical Analysis, Probability Distribution, Bayesian Network, R Programming, Statistical Programming, Statistical Tests

      3.8
      Rating, 3.8 out of 5 stars
      ·
      794 reviews

      Intermediate · Course · 1 - 3 Months

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      J

      Johns Hopkins University

      Statistical Inference

      Skills you'll gain: General Statistics, Probability & Statistics, Probability Distribution, R Programming, Statistical Analysis, Statistical Programming, Statistical Tests, Basic Descriptive Statistics, Estimation, Data Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      4.4K reviews

      Mixed · Course · 1 - 4 Weeks

    Searches related to statistical modeling

    statistical modeling for data science applications
    modeling climate anomalies with statistical analysis
    1234…134

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

    • Statistical Modeling for Data Science Applications: University of Colorado Boulder
    • Linear Regression and Modeling : Duke University
    • Data Science: Statistics and Machine Learning: Johns Hopkins University
    • Statistics with Python: University of Michigan
    • Fitting Statistical Models to Data with Python: University of Michigan
    • Improving your statistical inferences: Eindhoven University of Technology
    • Statistics Foundations: Meta
    • Predictive Modeling, Model Fitting, and Regression Analysis: University of California, Irvine
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
    • Building Statistical Models in R: Linear Regression: Coursera Project Network

    Skills you can learn in Probability And Statistics

    R Programming (19)
    Inference (16)
    Linear Regression (12)
    Statistical Analysis (12)
    Statistical Inference (11)
    Regression Analysis (10)
    Biostatistics (9)
    Bayesian (7)
    Logistic Regression (7)
    Probability Distribution (7)
    Bayesian Statistics (6)
    Medical Statistics (6)

    Frequently Asked Questions about Statistical Modeling

    Statistical modeling is the process of using statistics to analyze data and make it usable for decision-making and analysis. It involves using computer programs to pull out numbers from data sets, identify trends, make predictions, and understand different situations. Most statistical modeling results in charts, graphs, or other types of reports to explain the data.‎

    It's important to learn statistical modeling for many reasons to comprehend related material as a student and fulfill work-related requirements. Students often need to learn it because it's used in many different academic fields to understand the material. Many occupations either involve doing statistical analysis or being able to use the output in order to make decisions. Because of the way that data is reported in the media, it's helpful for members of the general public to have some understanding of how statistical modeling works.‎

    Typical careers that use statistical modeling are medical research, scientific research, and financial analysis, but it is a component of many different fields. People working in marketing and sales use statistical modeling to build forecasts and generate prospects. Business managers use it to make decisions and to share information. Academics use it to understand trends and patterns in their fields. Journalists use statistical modeling to present information in their stories. By turning data into usable information, including charts and graphs, statistical modeling can add value to many different fields. People who understand the fundamentals of quantitative modeling and statistical model can expect the skill to enhance their careers.‎

    Taking online courses on Coursera can help you learn statistical modeling in different ways, depending on your interests and needs. Some courses cover the fundamentals of statistics, which may be helpful if you have little background in the area. Others look at how statistical modeling is done with different software packages, such as Python, Excel, or SAS. Many classes focus on the uses of statistical modeling in specific fields, such as artificial intelligence, finance, or history. All classes include projects that help you apply what you learn.‎

    Online Statistical Modeling courses offer a convenient and flexible way to enhance your knowledge or learn new Statistical Modeling skills. Choose from a wide range of Statistical Modeling courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Statistical Modeling, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    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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