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Regression Courses

Regression courses can help you learn statistical modeling, data analysis techniques, and how to interpret relationships between variables. You can build skills in linear regression, logistic regression, and understanding residuals, along with evaluating model performance. Many courses introduce tools like R, Python, and Excel, that support conducting analyses and visualizing data, allowing you to apply these skills in practical work such as predicting outcomes and making informed decisions based on data.


Popular Regression Courses and Certifications


  • J

    Johns Hopkins University

    Regression Models

    Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Logistic Regression, Data Science, Data Analysis, Statistical Methods, Model Evaluation, Predictive Modeling, Probability & Statistics, Statistical Inference, Statistical Hypothesis Testing, Probability Distribution

    ★ 4.4 (3.4K) · Mixed · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • G

    Google

    Regression Analysis: Simplify Complex Data Relationships

    Skills you'll gain: Regression Analysis, Statistical Hypothesis Testing, Logistic Regression, Statistical Analysis, Statistical Methods, Correlation Analysis, Predictive Modeling, Supervised Learning, Predictive Analytics, Statistical Modeling, Machine Learning, Model Evaluation, Variance Analysis, Python Programming

    ★ 4.7 (592) · Advanced · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • D

    Duke University

    Linear Regression and Modeling

    Skills you'll gain: Regression Analysis, R (Software), Statistical Programming, Statistical Software, Statistical Analysis, R Programming, Statistical Modeling, Statistical Inference, Correlation Analysis, Data Analysis, Statistical Methods, Model Evaluation, Mathematical Modeling, Statistics, Predictive Modeling, Probability & Statistics, Statistical Hypothesis Testing

    ★ 4.8 (1.8K) · Beginner · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • D

    DeepLearning.AI

    Supervised Machine Learning: Regression and Classification

    Skills you'll gain: Supervised Learning, Applied Machine Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, Model Training, NumPy, Machine Learning Algorithms, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Model Optimization, Regression Analysis, Algorithms

    ★ 4.9 (32K) · Beginner · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • M

    Meta

    Statistics Foundations

    Skills you'll gain: Bayesian Statistics, Descriptive Statistics, Statistical Hypothesis Testing, Statistical Inference, Statistical Software, Sampling (Statistics), Data Modeling, Statistics, Probability & Statistics, Statistical Analysis, Statistical Methods, Statistical Modeling, Marketing Analytics, Tableau Software, Data Analysis, Spreadsheet Software, Analytics, Descriptive Analytics, Time Series Analysis and Forecasting, Regression Analysis

    ★ 4.8 (395) · Beginner · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • I

    Illinois Tech

    Linear Regression

    Skills you'll gain: Statistical Inference, Regression Analysis, R Programming, Statistical Methods, Statistical Analysis, Statistical Modeling, R (Software), Statistical Software, Data Science, Correlation Analysis, Data Analysis, Probability & Statistics, Linear Algebra

    ★ 4.6 (30) · Intermediate · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree

What brings you to Coursera today?

  • C

    Coursera

    Predict and Validate Regression Models in R

    Skills you'll gain: Regression Analysis, Predictive Modeling, Model Evaluation, Statistical Modeling, Predictive Analytics, R Programming, Financial Forecasting, Statistical Methods, Model Training, Data Validation, Verification And Validation, Plot (Graphics), Performance Metric

    Beginner · Course · 1 - 4 Weeks

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • C

    Corporate Finance Institute

    Regression Analysis - Fundamentals & Practical Applications

    Skills you'll gain: Regression Analysis, Correlation Analysis, Advanced Analytics, Statistical Methods, Statistical Modeling, Statistical Analysis, Predictive Modeling, Data Analysis, Scikit Learn (Machine Learning Library), Microsoft Excel, Statistical Programming, Data Analysis Software, Model Evaluation

    Advanced · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • U

    University of Pittsburgh

    Linear Algebra and Regression Fundamentals for Data Science

    Skills you'll gain: NumPy, Matplotlib, Plot (Graphics), Linear Algebra, Pandas (Python Package), Data Manipulation, Applied Mathematics, Python Programming, Data Analysis, Data Science, Mathematical Software, Regression Analysis, Data Visualization Software, Mathematics and Mathematical Modeling, Probability & Statistics, Numerical Analysis, Mathematical Modeling, Machine Learning, Computational Logic, Logical Reasoning

    ★ 3.9 (8) · Beginner · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • C

    Coursera

    Linear Regression with Python

    Skills you'll gain: Model Training, Regression Analysis, NumPy, Machine Learning Algorithms, Machine Learning, Model Optimization, Deep Learning, Data Science, Python Programming

    ★ 4.6 (440) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Scikit-Learn to Solve Regression Machine Learning Problems

    Skills you'll gain: Scikit Learn (Machine Learning Library), Predictive Modeling, Regression Analysis, Machine Learning Algorithms, Applied Machine Learning, Predictive Analytics, Python Programming, Classification Algorithms, Model Training, Machine Learning, Data Analysis

    ★ 4.8 (11) · Beginner · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • I

    IBM

    Supervised Machine Learning: Regression

    Skills you'll gain: Supervised Learning, Regression Analysis, Applied Machine Learning, Predictive Modeling, Machine Learning Methods, Model Training, Statistical Machine Learning, Machine Learning, Machine Learning Algorithms, Statistical Modeling, Model Evaluation, Data Preprocessing, Feature Engineering, Model Optimization, Statistical Analysis, Statistical Methods, Classification Algorithms, Data Presentation

    ★ 4.7 (833) · Intermediate · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
1234…58

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

  • Regression Models: Johns Hopkins University
  • Regression Analysis: Simplify Complex Data Relationships: Google
  • Linear Regression and Modeling : Duke University
  • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
  • Statistics Foundations: Meta
  • Linear Regression: Illinois Tech
  • Predict and Validate Regression Models in R: Coursera
  • Regression Analysis - Fundamentals & Practical Applications: Corporate Finance Institute
  • Linear Algebra and Regression Fundamentals for Data Science: University of Pittsburgh
  • Linear Regression with Python: Coursera

Frequently Asked Questions about Regression

Regression is a statistical method used to understand relationships between variables. It helps in predicting outcomes based on input data, making it a crucial tool in various fields such as finance, healthcare, and marketing. By analyzing historical data, regression allows professionals to make informed decisions, identify trends, and forecast future events. Understanding regression is important because it provides insights that can lead to better strategies and improved performance in business and research.‎

Careers in regression span a variety of fields, including data analysis, statistics, finance, and machine learning. Job titles may include Data Analyst, Statistician, Business Analyst, and Data Scientist. These roles often require the ability to interpret data and apply regression techniques to solve complex problems. As organizations increasingly rely on data-driven decision-making, professionals skilled in regression are in high demand.‎

To effectively learn regression, you should focus on several key skills. First, a solid understanding of statistics is essential, as regression is grounded in statistical principles. Additionally, proficiency in programming languages such as Python or R is beneficial for implementing regression models. Familiarity with data visualization tools and techniques will also help you interpret and present your findings clearly. Lastly, knowledge of machine learning concepts can enhance your ability to apply regression in predictive analytics.‎

There are numerous online courses available to help you learn regression. Some notable options include Build Regression, Classification, and Clustering Models and Linear Regression. These courses cover various aspects of regression, from foundational concepts to advanced applications, making them suitable for learners at different levels.‎

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

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

To learn regression effectively, start by selecting a course that matches your current skill level and goals. Engage with the course materials, complete assignments, and practice with real datasets to reinforce your understanding. Additionally, consider joining online forums or study groups to discuss concepts and share insights with peers. Regular practice and application of regression techniques will help solidify your knowledge and build confidence.‎

Typical topics covered in regression courses include linear regression, logistic regression, model validation, and variable selection. You may also explore advanced topics such as nonparametric regression and generalized linear models. Courses often incorporate practical applications, allowing you to work with datasets and use software tools to implement regression techniques effectively.‎

For training and upskilling employees in regression, courses like Excel Regression Models for Business Forecasting and Variable Selection, Model Validation, Nonlinear Regression are excellent choices. These courses provide practical skills that can be directly applied in the workplace, enhancing employees' ability to analyze data and make informed decisions.‎

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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