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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 Michigan

      Linear Regression Modeling for Health Data

      Skills you'll gain: Statistical Modeling, Regression Analysis, Statistical Methods, Statistical Inference, Probability & Statistics, Correlation Analysis, Data Analysis, Statistical Analysis, Statistical Hypothesis Testing, Predictive Modeling

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      U

      University of Colorado System

      Regression Modeling for Marketers

      Skills you'll gain: Statistical Software, Marketing Analytics, Regression Analysis, Statistical Modeling, Data Visualization, Statistical Analysis, Market Analysis, Marketing, Predictive Analytics, Marketing Strategies, Predictive Modeling, Customer Insights, Market Dynamics, Statistical Hypothesis Testing, A/B Testing

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: Free Trial
      Free Trial
      S

      Simplilearn

      Supervised Learning Regression Classification Clustering

      Skills you'll gain: Supervised Learning, Data Modeling, Data Analysis, Regression Analysis, Unsupervised Learning, Classification And Regression Tree (CART), Machine Learning Algorithms, Machine Learning, Predictive Modeling, Predictive Analytics, Random Forest Algorithm, Bayesian Statistics

      Beginner · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      IBM

      Data Analysis with Python

      Skills you'll gain: Predictive Modeling, Exploratory Data Analysis, Data Wrangling, Data Analysis, Data Import/Export, Pandas (Python Package), Statistical Modeling, Data-Driven Decision-Making, Data Manipulation, Scikit Learn (Machine Learning Library), Regression Analysis, Feature Engineering, Matplotlib, Data Cleansing, NumPy, Descriptive Statistics, Supervised Learning, Data Pipelines

      4.7
      Rating, 4.7 out of 5 stars
      ·
      19K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      N
      G
      N
      G

      Multiple educators

      Machine Learning for Trading

      Skills you'll gain: Tensorflow, Keras (Neural Network Library), Machine Learning, Google Cloud Platform, Applied Machine Learning, Financial Trading, Reinforcement Learning, Supervised Learning, Data Pipelines, Time Series Analysis and Forecasting, Statistical Machine Learning, Technical Analysis, Deep Learning, Portfolio Management, Machine Learning Methods, Artificial Neural Networks, Market Trend, Securities Trading, Artificial Intelligence and Machine Learning (AI/ML), Financial Market

      3.9
      Rating, 3.9 out of 5 stars
      ·
      1.1K reviews

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      Machine Learning with Python

      Skills you'll gain: Supervised Learning, Feature Engineering, Jupyter, Unsupervised Learning, Scikit Learn (Machine Learning Library), Python Programming, Predictive Modeling, Machine Learning, Dimensionality Reduction, Classification And Regression Tree (CART), Matplotlib, NumPy, Regression Analysis, Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      17K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      V

      Vanderbilt University

      Prompt Engineering

      Skills you'll gain: Prompt Engineering, ChatGPT, Ideation, Verification And Validation, Data Validation, Data Presentation, Productivity, Document Management, Generative AI, Artificial Intelligence, Large Language Modeling, Risk Management Framework, Microsoft Excel, Creative Thinking, Ingenuity, Brainstorming, Problem Solving, Data Ethics, Data Analysis, Information Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6.6K reviews

      Beginner · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      K

      Kennesaw State University

      Six Sigma Yellow Belt

      Skills you'll gain: Six Sigma Methodology, Root Cause Analysis, Lean Methodologies, Data Collection, Process Improvement, Quality Improvement, Process Optimization, Lean Six Sigma, Correlation Analysis, Statistical Hypothesis Testing, Kaizen Methodology, Process Analysis, Probability Distribution, Regression Analysis, Process Capability, Business Process, Statistical Process Controls, Quality Management, Team Management, Continuous Improvement Process

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.9K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      M

      Macquarie University

      Excel Skills for Business Forecasting

      Skills you'll gain: Time Series Analysis and Forecasting, Forecasting, Financial Forecasting, Regression Analysis, Microsoft Excel, Demand Planning, Excel Formulas, Trend Analysis, Business Mathematics, Predictive Modeling, Business Metrics, Strategic Thinking, Data Presentation, Statistical Visualization, Graphing, Market Trend, Business Economics, Key Performance Indicators (KPIs), Statistical Modeling, Exploratory Data Analysis

      4.9
      Rating, 4.9 out of 5 stars
      ·
      304 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      M

      Microsoft

      Microsoft Azure AI Fundamentals AI-900 Exam Prep

      Skills you'll gain: Anomaly Detection, Image Analysis, Text Mining, Unsupervised Learning, Computer Vision, Artificial Intelligence, Natural Language Processing, Microsoft Azure, Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Data Ethics, Machine Learning, Large Language Modeling, Regression Analysis, Predictive Modeling, Machine Learning Methods, Statistical Machine Learning, Machine Learning Algorithms, Classification And Regression Tree (CART), Automation

      4.5
      Rating, 4.5 out of 5 stars
      ·
      1.2K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      I

      IBM

      IBM Business Intelligence (BI) Analyst

      Skills you'll gain: Dashboard, Data Storytelling, Data Warehousing, SQL, Database Design, MySQL, Presentations, Descriptive Statistics, Extract, Transform, Load, Business Intelligence, IBM DB2, Relational Databases, Tableau Software, Stored Procedure, Data Visualization Software, Interactive Data Visualization, Regression Analysis, Data-Driven Decision-Making, Excel Formulas, Microsoft Excel

      4.7
      Rating, 4.7 out of 5 stars
      ·
      14K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • 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

    Regression learners also search

    Regression Analysis
    Regression Models
    Linear Regression
    Logistic Regression
    Predictive Modeling
    Statistical Modeling
    Predictive Analytics
    Data Modeling
    1234…45

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

    • Linear Regression Modeling for Health Data: University of Michigan
    • Regression Modeling for Marketers: University of Colorado System
    • Supervised Learning Regression Classification Clustering: Simplilearn
    • Data Analysis with Python: IBM
    • Machine Learning for Trading: New York Institute of Finance
    • Machine Learning with Python: IBM
    • Prompt Engineering: Vanderbilt University
    • Six Sigma Yellow Belt: Kennesaw State University
    • Excel Skills for Business Forecasting: Macquarie University
    • Microsoft Azure AI Fundamentals AI-900 Exam Prep: Microsoft

    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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