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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
      P

      Packt

      Advanced Automation Frameworks and Continuous Integration

      Skills you'll gain: GitHub, Jenkins, Git (Version Control System), Version Control, Selenium (Software), CI/CD, Continuous Integration, Test Automation, Test Data, Unit Testing, Test Case, Software Testing, Regression Testing, Scenario Testing, Test Execution Engine, Python Programming, Automation, Debugging

      Advanced · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      P

      Packt

      Intermediate Data Manipulation and Machine Learning

      Skills you'll gain: Regression Analysis, Dimensionality Reduction, Reinforcement Learning, Artificial Intelligence, Data Mining, Machine Learning, Applied Machine Learning, Classification And Regression Tree (CART), Statistical Analysis, Predictive Modeling, Supervised Learning, Unsupervised Learning, Feature Engineering, Random Forest Algorithm, Data Manipulation

      Intermediate · Course · 3 - 6 Months

    • F

      Fundação Instituto de Administração

      Decision Making

      Skills you'll gain: Decision Making, Strategic Decision-Making, Data-Driven Decision-Making, Behavioral Economics, Analysis, Business Risk Management, Complex Problem Solving, Risk Analysis, Systems Thinking, Organizational Strategy, Decision Tree Learning, Virtual Teams, Psychology, Probability

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      G

      Google Cloud

      Serverless Data Processing with Dataflow: Operations en Español

      Skills you'll gain: Dataflow, Data Pipelines, Performance Tuning, Site Reliability Engineering, Data Infrastructure, Maintainability, Debugging, System Monitoring, Data Processing, CI/CD, Unit Testing, Google Cloud Platform, Disaster Recovery, Java

      Advanced · Course · 1 - 3 Months

    • P

      Packt

      Complete SAS Guide - Learn SAS and Become a Data Ninja

      Skills you'll gain: SAS (Software), Data Import/Export, Predictive Modeling, Data Manipulation, Statistical Analysis, SQL, Feature Engineering, Data Analysis, R Programming, Data Cleansing, Regression Analysis, Software Installation, Statistical Hypothesis Testing, System Configuration

      Beginner · Course · 3 - 6 Months

    • Status: New
      New
      Status: Free Trial
      Free Trial
      P

      Packt

      Exploratory Data Analysis & Core ML Algorithms

      Skills you'll gain: Exploratory Data Analysis, Data Processing, Predictive Modeling, Classification And Regression Tree (CART), Data Analysis, Machine Learning Algorithms, Regression Analysis, Statistical Modeling, Data Cleansing, Feature Engineering, Supervised Learning, Data Manipulation, Predictive Analytics, Data Visualization

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      C

      Clemson University

      Applied Data Science

      Skills you'll gain: Dimensionality Reduction, Data Cleansing, Matplotlib, Regression Analysis, Unsupervised Learning, Data Science, Statistical Analysis, Statistical Methods, Descriptive Statistics, Data Analysis, Data Visualization Software, Applied Machine Learning, Pandas (Python Package), Big Data, Data-Driven Decision-Making, Exploratory Data Analysis, Predictive Modeling, Machine Learning Algorithms, Machine Learning, Supervised Learning

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      O

      O.P. Jindal Global University

      Econometrics - Theory and Practice

      Skills you'll gain: Econometrics, Regression Analysis, Statistical Hypothesis Testing, Statistical Modeling, Quantitative Research, Statistical Methods, Stata, Time Series Analysis and Forecasting, Correlation Analysis, Statistical Inference, Statistical Analysis, Probability & Statistics, Data Manipulation, Probability Distribution

      Beginner · Course · 1 - 3 Months

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

      Simplilearn

      Business Analytics using Excel

      Skills you'll gain: Statistical Analysis, Microsoft Excel, Excel Formulas, Pivot Tables And Charts, Dashboard, Excel Macros, Business Analytics, Spreadsheet Software, Data Analysis, Analytics, Data Cleansing, Data Processing, Regression Analysis, Data Validation, Productivity Software, Descriptive Statistics

      Beginner · Course · 1 - 4 Weeks

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

      Simplilearn

      Lean Management and Six Sigma Green Belt Program

      Skills you'll gain: Lean Manufacturing, Lean Six Sigma, Lean Methodologies, Process Mapping, Statistical Process Controls, Six Sigma Methodology, Kaizen Methodology, Operational Efficiency, Process Improvement, Performance Measurement, Manufacturing Operations, Business Process, Quality Management, Operational Excellence, Statistical Analysis, Performance Metric, Waste Minimization, Kanban Principles, Process Capability, Continuous Improvement Process

      Beginner · Specialization · 1 - 3 Months

    • H

      H2O.ai

      DataPrep for H2O Driverless AI

      Skills you'll gain: Supervised Learning, Machine Learning, Time Series Analysis and Forecasting, Data Quality, Unsupervised Learning, Data Cleansing, Data Analysis, Feature Engineering, Pandas (Python Package), Data Transformation, Exploratory Data Analysis, Classification And Regression Tree (CART), Regression Analysis

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: Free Trial
      Free Trial
      P

      Packt

      Advanced ML Algorithms & Unsupervised Learning

      Skills you'll gain: Dimensionality Reduction, Unsupervised Learning, Deep Learning, Random Forest Algorithm, Machine Learning Algorithms, Machine Learning, Decision Tree Learning, Classification And Regression Tree (CART), Feature Engineering, Supervised Learning, Statistical Machine Learning, Predictive Modeling, Artificial Intelligence, Exploratory Data Analysis

      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…42434445

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

    • Advanced Automation Frameworks and Continuous Integration: Packt
    • Intermediate Data Manipulation and Machine Learning: Packt
    • Decision Making: Fundação Instituto de Administração
    • Serverless Data Processing with Dataflow: Operations en Español: Google Cloud
    • Complete SAS Guide - Learn SAS and Become a Data Ninja: Packt
    • Exploratory Data Analysis & Core ML Algorithms: Packt
    • Applied Data Science: Clemson University
    • Econometrics - Theory and Practice: O.P. Jindal Global University
    • Business Analytics using Excel: Simplilearn
    • Lean Management and Six Sigma Green Belt Program: Simplilearn

    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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