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.

Johns Hopkins University
Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Logistic Regression, Data Analysis, Model Evaluation, Probability & Statistics, Statistical Inference
Mixed · Course · 1 - 4 Weeks

Skills you'll gain: Regression Analysis, Logistic Regression, Statistical Hypothesis Testing, Data Analysis, Advanced Analytics, Statistical Analysis, Correlation Analysis, Analytical Skills, Business Analytics, Statistical Modeling, Model Evaluation, Variance Analysis, Predictive Modeling, Machine Learning, Python Programming
Advanced · Course · 1 - 3 Months

DeepLearning.AI
Skills you'll gain: Supervised Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Regression Analysis, Unsupervised Learning
Beginner · Course · 1 - 4 Weeks

Duke University
Skills you'll gain: Regression Analysis, R (Software), Data Analysis Software, Statistical Analysis, R Programming, Statistical Modeling, Statistical Inference, Correlation Analysis, Model Evaluation, Exploratory Data Analysis, Mathematical Modeling, Statistics, Predictive Modeling, Probability & Statistics
Beginner · Course · 1 - 4 Weeks

Coursera
Skills you'll gain: Regression Analysis, NumPy, Supervised Learning, Machine Learning Algorithms, Machine Learning, Predictive Modeling, Deep Learning, Data Science, Python Programming
Intermediate · Guided Project · Less Than 2 Hours

Dartmouth College
Skills you'll gain: Supervised Learning, Predictive Modeling, Logistic Regression, Statistical Modeling, Model Evaluation, Machine Learning, Machine Learning Algorithms, Classification Algorithms, Regression Analysis, Probability & Statistics, Linear Algebra
Build toward a degree
Intermediate · Course · 1 - 3 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing
Beginner · Specialization · 1 - 3 Months

Corporate Finance Institute
Skills you'll gain: Regression Analysis, Statistical Modeling, Statistical Analysis, Predictive Modeling, Data Analysis, Scikit Learn (Machine Learning Library), Microsoft Excel, Linear Algebra, Model Evaluation, Supervised Learning, Exploratory Data Analysis
Advanced · Course · 1 - 3 Months

Skills you'll gain: Bayesian Statistics, Descriptive Statistics, Statistical Hypothesis Testing, Statistical Inference, Sampling (Statistics), Data Modeling, Statistics, Probability & Statistics, Statistical Analysis, Statistical Methods, Statistical Modeling, Marketing Analytics, Tableau Software, Data Analysis, Spreadsheet Software, Analytics, Time Series Analysis and Forecasting, Regression Analysis
Beginner · Course · 1 - 3 Months

Illinois Tech
Skills you'll gain: Statistical Inference, Regression Analysis, R Programming, Statistical Analysis, Statistical Modeling, R (Software), Data Science, Logistic Regression, Data Analysis, Probability & Statistics, Linear Algebra
Build toward a degree
Intermediate · Course · 1 - 4 Weeks

University of Pittsburgh
Skills you'll gain: NumPy, Matplotlib, Linear Algebra, Pandas (Python Package), Data Manipulation, Applied Mathematics, Data Visualization, Python Programming, Data Analysis, Data Science, Regression Analysis, Data Visualization Software, Mathematics and Mathematical Modeling, Probability & Statistics, Statistics, Numerical Analysis, Mathematical Modeling, Machine Learning, Computational Logic, Logical Reasoning
Build toward a degree
Beginner · Course · 1 - 4 Weeks

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, Machine Learning, Data Analysis
Beginner · Guided Project · Less Than 2 Hours
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:
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.‎