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    Regression Courses Online

    Master regression analysis for predictive modeling. Learn about linear, logistic, and polynomial regression techniques.

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    Explore the Regression Course Catalog

    • Status: Free Trial
      Free Trial
      U

      University of Illinois Urbana-Champaign

      Managerial Economics and Business Analysis

      Skills you'll gain: Descriptive Statistics, Supply And Demand, Market Dynamics, Sampling (Statistics), Statistical Inference, Business Analytics, Financial Systems, Financial Policy, Banking, Probability Distribution, Analytics, Statistical Analysis, Statistical Hypothesis Testing, Statistics, Regression Analysis, Microsoft Excel, Economics, Financial Market, Business Economics, Risk Management

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      4.1K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Virginia

      Marketing Analytics

      Skills you'll gain: Marketing Analytics, Marketing Effectiveness, Marketing, Marketing Strategies, Regression Analysis, Data-Driven Decision-Making, Strategic Marketing, Brand Management, Resource Allocation, Customer Insights, Predictive Analytics, Advertising Campaigns, Statistical Analysis, A/B Testing, Consumer Behaviour, Return On Investment

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.4K reviews

      Beginner · Course · 1 - 3 Months

    • E

      EDUCBA

      Regression & Forecasting for Data Scientists using Python

      Skills you'll gain: Time Series Analysis and Forecasting, Exploratory Data Analysis, Feature Engineering, Statistical Analysis, Forecasting, Regression Analysis, Python Programming, Data Analysis, Predictive Modeling, Pandas (Python Package), Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Supervised Learning, Data Cleansing, Data Transformation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      39 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Data Science: Statistics and Machine Learning

      Skills you'll gain: Shiny (R Package), Rmarkdown, Regression Analysis, Leaflet (Software), Exploratory Data Analysis, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Plotly, Machine Learning Algorithms, Interactive Data Visualization, Probability & Statistics, Data Visualization, Statistical Machine Learning, Feature Engineering, Statistical Analysis, Statistical Modeling, Probability, Data Science, Data Analysis

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

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of California San Diego

      Big Data

      Skills you'll gain: Apache Spark, Apache Hadoop, Data Integration, Exploratory Data Analysis, Big Data, Graph Theory, Data Pipelines, Database Design, Data Modeling, Regression Analysis, Applied Machine Learning, Data Presentation, Scalability, Data Mining, Data Processing, Statistical Analysis, Data Management, NoSQL, Database Management Systems, Network Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      14K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      I

      Illinois Tech

      Variable Selection, Model Validation, Nonlinear Regression

      Skills you'll gain: Statistical Inference, Regression Analysis, Statistical Methods, R Programming, Statistical Analysis, Statistical Modeling, Predictive Modeling, Advanced Analytics, Probability & Statistics, Data Validation

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      7 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      H

      Howard University

      Building Regression Models with Linear Algebra

      Skills you'll gain: Regression Analysis, Predictive Modeling, Statistical Modeling, Supervised Learning, Scikit Learn (Machine Learning Library), Applied Mathematics, Machine Learning Methods, Linear Algebra, Small Data, Statistical Analysis, NumPy

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Michigan

      Foundations of Sports Analytics: Data, Representation, and Models in Sports

      Skills you'll gain: Statistical Methods, Regression Analysis, Data Cleansing, Statistical Hypothesis Testing, Correlation Analysis, Matplotlib, Data Manipulation, Data Visualization, Statistical Analysis, Scatter Plots, Probability & Statistics, R Programming, Data Analysis, Descriptive Statistics, Pandas (Python Package), Python Programming

      4.4
      Rating, 4.4 out of 5 stars
      ·
      187 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Machine Learning

      Skills you'll gain: Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Modeling, Artificial Intelligence, Deep Learning, Data Mining, Computer Vision, Statistical Machine Learning, Predictive Analytics, Text Mining, Machine Learning Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      16K reviews

      Intermediate · Specialization · 3 - 6 Months

    • E

      Erasmus University Rotterdam

      Econometrics: Methods and Applications

      Skills you'll gain: Econometrics, Time Series Analysis and Forecasting, Regression Analysis, Data Analysis, Statistical Analysis, Quantitative Research, Statistical Modeling, Statistics, Predictive Analytics, Probability, Linear Algebra, Peer Review

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.2K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      Mathematics for Machine Learning and Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Linear Algebra, Statistical Inference, Applied Mathematics, NumPy, Calculus, Dimensionality Reduction, Numerical Analysis, Mathematical Modeling, Machine Learning, Machine Learning Methods, Python Programming, Jupyter, Data Manipulation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.7K reviews

      Intermediate · Specialization · 1 - 3 Months

    • P

      Packt

      Regression Analysis for Statistics & Machine Learning in R

      Skills you'll gain: Regression Analysis, Data Cleansing, R Programming, Applied Machine Learning, Statistical Analysis, Data Manipulation, Statistical Machine Learning, Classification And Regression Tree (CART), Tidyverse (R Package), Advanced Analytics, Statistical Modeling, Random Forest Algorithm, Data Transformation, Statistical Methods, Predictive Modeling, Exploratory Data Analysis, Feature Engineering, Machine Learning, Dimensionality Reduction

      Intermediate · Course · 1 - 3 Months

    Regression learners also search

    Regression Analysis
    Regression Models
    Linear Regression
    Logistic Regression
    Predictive Modeling
    Statistical Modeling
    Predictive Analytics
    Data Modeling
    1…567…45

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

    • Managerial Economics and Business Analysis: University of Illinois Urbana-Champaign
    • Marketing Analytics: University of Virginia
    • Regression & Forecasting for Data Scientists using Python: EDUCBA
    • Data Science: Statistics and Machine Learning: Johns Hopkins University
    • Big Data: University of California San Diego
    • Variable Selection, Model Validation, Nonlinear Regression: Illinois Tech
    • Building Regression Models with Linear Algebra: Howard University
    • Foundations of Sports Analytics: Data, Representation, and Models in Sports: University of Michigan
    • Machine Learning: University of Washington
    • Econometrics: Methods and Applications: Erasmus University Rotterdam

    Frequently Asked Questions about Regression

    Regression is a statistical technique used in data analysis to model the relationship between a dependent variable and one or more independent variables. It is commonly used to predict or estimate the value of the dependent variable based on the values of the independent variables. In simpler terms, regression helps us understand how the change in one variable can affect the other variable(s). It is widely used in various fields, including economics, finance, psychology, and machine learning.‎

    To learn regression, you need to acquire the following skills:

    1. Statistics: Understanding statistical concepts such as mean, median, variance, and correlation is essential for regression analysis. Familiarize yourself with concepts like hypothesis testing, p-values, and confidence intervals.

    2. Mathematics: A solid foundation in calculus and linear algebra is crucial for regression analysis. Understanding concepts like derivatives, matrices, and vectors will help you grasp regression models more effectively.

    3. Programming: Proficiency in a programming language is necessary for implementing regression models. Python and R are commonly used languages in data science, which offer various libraries and packages for regression analysis.

    4. Data Analysis: Learning data manipulation and exploratory data analysis techniques are essential for regression. Gain skills in cleaning, transforming, and visualizing data using tools like pandas, NumPy, and matplotlib.

    5. Machine Learning: Regression is a machine learning technique, so having a basic understanding of machine learning algorithms and concepts like supervised learning, model evaluation, and overfitting is beneficial.

    6. Regression Models: Familiarize yourself with different regression models such as linear regression, polynomial regression, logistic regression, and ridge regression. Learn how to interpret and evaluate these models.

    7. Feature Selection: Understand methods to identify and select relevant features for regression analysis. Techniques like stepwise regression, LASSO, and principal component analysis (PCA) can help in determining the most important predictors.

    8. Model Evaluation: Learn how to assess the performance of your regression models using metrics like mean squared error (MSE), R-squared value, and adjusted R-squared. Cross-validation techniques like k-fold cross-validation are also valuable.

    9. Domain Knowledge: Having a basic understanding of the domain in which you are applying regression is advantageous. It helps in interpreting the results correctly and making informed decisions based on the analysis.

    10. Critical Thinking and Problem Solving: Developing strong analytical and problem-solving skills will aid you in analyzing data, selecting appropriate regression models, and interpreting the results accurately.‎

    With regression skills, there are various job opportunities in different industries. Some of the most common jobs that require regression skills include:

    1. Data Scientist: Regression analysis is an essential tool for data scientists to uncover relationships between variables and make predictions. They use regression to build models that provide insights and recommendations based on data analysis.

    2. Statistician: Statisticians utilize regression analysis to interpret data, identify trends, and make predictions. They work in a wide range of fields such as research, healthcare, government, finance, and marketing.

    3. Financial Analyst: Regression skills are highly valuable for financial analysts who need to understand and predict market trends, stock prices, and investment performance. Regression analysis helps them make informed decisions and develop models for forecasting.

    4. Market Research Analyst: Regression analysis is widely used in market research to evaluate consumer behavior, predict sales, and estimate market demand. Market research analysts employ regression models to analyze and interpret data for strategic decision-making.

    5. Business Analyst: Business analysts rely on regression analysis to identify patterns, relationships, and trends in data. They use this information to provide insights, optimize business processes, forecast sales, and improve organizational performance.

    6. Actuary: Actuaries apply regression analysis to calculate and assess risk in insurance and finance industries. They develop models to predict and manage risks related to life expectancy, insurance claims, and property damage.

    7. Operations Research Analyst: Regression skills are crucial for operations research analysts, who use statistical models to optimize processes and solve complex problems. Regression analysis helps them make data-driven decisions, improve efficiency, and increase profitability.

    8. Marketing Analyst: Marketers utilize regression analysis to understand consumer behavior, segment target markets, and predict customer preferences. Regression skills are essential for developing effective marketing strategies and measuring the impact of promotional activities.

    9. Epidemiologist: Regression analysis plays a significant role in epidemiology to study the relationships between risk factors, diseases, and health outcomes. Epidemiologists use regression models to identify the impact of various factors on disease occurrence and prevalence.

    10. Environmental Scientist: Regression skills are valuable in environmental science for analyzing and predicting the impact of environmental factors on ecosystems. Environmental scientists employ regression analysis to interpret data related to pollution, climate change, and biodiversity.

    These are just a few examples of the wide range of job opportunities that you can pursue with regression skills. The demand for regression expertise is growing rapidly, making it an excellent skill to acquire for various industries.‎

    People who are analytical, detail-oriented, and have a strong background in mathematics and statistics are best suited for studying Regression. Additionally, individuals who are interested in data analysis, predictive modeling, and making informed decisions based on data would find studying Regression beneficial.‎

    Here are some topics that are related to Regression that you can study:

    1. Simple Linear Regression: Understanding the basic concepts and techniques of simple linear regression, which involves predicting a dependent variable based on one independent variable.

    2. Multiple Linear Regression: Expanding upon simple linear regression, multiple linear regression involves predicting a dependent variable based on two or more independent variables.

    3. Polynomial Regression: Exploring the concept of polynomial regression, which allows for fitting a curved line to a dataset by including polynomial terms.

    4. Logistic Regression: Investigating logistic regression, which is used when the dependent variable is categorical, providing insights into predicting a binary outcome.

    5. Time Series Analysis: Examining time series analysis, which involves analyzing and predicting data points collected over a period of time using regression techniques.

    6. Ridge Regression: Delving into ridge regression, a technique that helps prevent overfitting by penalizing large or complex models.

    7. Lasso Regression: Understanding lasso regression, which aids in feature selection by shrinking coefficients and encouraging simpler models.

    8. Elastic Net Regression: Learning about elastic net regression, which combines both ridge and lasso regression techniques to improve model performance.

    9. Generalized Linear Models: Exploring generalized linear models, a broader framework that includes different regression models for various types of dependent variables (e.g., Poisson regression, exponential regression).

    10. Bayesian Regression: Diving into Bayesian regression, which incorporates prior knowledge into the regression model through Bayesian inference.

    These topics provide a solid foundation for understanding and applying regression techniques and can further enhance your knowledge in this area.‎

    Online Regression courses offer a convenient and flexible way to enhance your knowledge or learn new Regression is a statistical technique used in data analysis to model the relationship between a dependent variable and one or more independent variables. It is commonly used to predict or estimate the value of the dependent variable based on the values of the independent variables. In simpler terms, regression helps us understand how the change in one variable can affect the other variable(s). It is widely used in various fields, including economics, finance, psychology, and machine learning. skills. Choose from a wide range of Regression courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Regression, 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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