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    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
      G

      Google Cloud

      Introduction to Trading, Machine Learning & GCP

      Skills you'll gain: Machine Learning, Google Cloud Platform, Applied Machine Learning, Supervised Learning, Time Series Analysis and Forecasting, Financial Trading, Deep Learning, Statistical Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Artificial Neural Networks, Securities Trading, Technical Analysis, Financial Forecasting, Quantitative Research, Financial Modeling, Forecasting, Regression Analysis

      4
      Rating, 4 out of 5 stars
      ·
      874 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Practical Machine Learning

      Skills you'll gain: Predictive Modeling, Machine Learning Algorithms, Statistical Machine Learning, Feature Engineering, Supervised Learning, Classification And Regression Tree (CART), Applied Machine Learning, Decision Tree Learning, Machine Learning, Random Forest Algorithm, Regression Analysis, Data Processing, Data Collection

      4.5
      Rating, 4.5 out of 5 stars
      ·
      3.3K reviews

      Mixed · Course · 1 - 4 Weeks

    • D

      Duke University

      Bayesian Statistics

      Skills you'll gain: Bayesian Statistics, Statistical Hypothesis Testing, Statistical Modeling, Statistical Methods, Statistical Inference, Statistical Analysis, Regression Analysis, Data Analysis, R Programming, Probability, Data-Driven Decision-Making, Probability Distribution

      3.8
      Rating, 3.8 out of 5 stars
      ·
      797 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Minnesota

      Introduction to Predictive Modeling

      Skills you'll gain: Time Series Analysis and Forecasting, Predictive Modeling, Regression Analysis, Microsoft Excel, Forecasting, Pivot Tables And Charts, Data Transformation, Trend Analysis, Data Cleansing, Statistical Methods, Performance Metric

      4.8
      Rating, 4.8 out of 5 stars
      ·
      135 reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      Q

      Queen Mary University of London

      Market Research

      Skills you'll gain: Qualitative Research, Proposal Development, Market Research, Research Reports, Data Collection, Research Design, Research Methodologies, Data Analysis, Statistical Hypothesis Testing, Survey Creation, Statistical Analysis, Surveys, Correlation Analysis, Quantitative Research, Research, Science and Research, Market Analysis, Focus Group, Regression Analysis, Content Performance Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      462 reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Pennsylvania

      A Crash Course in Causality: Inferring Causal Effects from Observational Data

      Skills you'll gain: R Programming, Statistical Analysis, Statistical Methods, Statistical Software, Statistical Modeling, Statistical Inference, Data Analysis, Probability & Statistics, Regression Analysis, Research Design, Graph Theory

      4.7
      Rating, 4.7 out of 5 stars
      ·
      564 reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      N

      New York University

      Machine Learning and Reinforcement Learning in Finance

      Skills you'll gain: Supervised Learning, Reinforcement Learning, Applied Machine Learning, Machine Learning, Statistical Methods, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Artificial Neural Networks, Decision Tree Learning, Predictive Modeling, Financial Trading, Financial Market, Derivatives, Scikit Learn (Machine Learning Library), Markov Model, Regression Analysis, Deep Learning, Market Liquidity, Financial Services

      3.7
      Rating, 3.7 out of 5 stars
      ·
      814 reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      K

      Kennesaw State University

      Six Sigma Advanced Define and Measure Phases

      Skills you'll gain: Process Capability, Team Management, Statistical Process Controls, Exploratory Data Analysis, Six Sigma Methodology, Probability & Statistics, Process Analysis, Statistical Analysis, Lean Six Sigma, Process Mapping, Correlation Analysis, Data Analysis, Data Collection, Regression Analysis, Process Improvement, Quality Improvement, Business Process, Graphing

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.3K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      W

      Wesleyan University

      Machine Learning for Data Analysis

      Skills you'll gain: Classification And Regression Tree (CART), Decision Tree Learning, Predictive Modeling, Random Forest Algorithm, Applied Machine Learning, Predictive Analytics, Unsupervised Learning, Machine Learning, Supervised Learning, Data Analysis, Data Mining, Feature Engineering, Exploratory Data Analysis, Regression Analysis, Statistical Analysis, Statistical Methods

      4.2
      Rating, 4.2 out of 5 stars
      ·
      324 reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      AI for Medical Prognosis

      Skills you'll gain: Risk Modeling, Decision Tree Learning, Predictive Modeling, Feature Engineering, Applied Machine Learning, Random Forest Algorithm, Forecasting, Machine Learning, Statistical Methods, Statistical Analysis, Probability & Statistics, Data Analysis, Regression Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      787 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      A

      Alberta Machine Intelligence Institute

      Machine Learning: Algorithms in the Real World

      Skills you'll gain: Supervised Learning, Feature Engineering, Machine Learning Algorithms, Data Ethics, Applied Machine Learning, Data Quality, Data Processing, MLOps (Machine Learning Operations), Jupyter, Data Validation, Machine Learning, Business Operations, Data Cleansing, Product Lifecycle Management, Ethical Standards And Conduct, Classification And Regression Tree (CART), Data Transformation, Operational Analysis, Verification And Validation, Business Strategy

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.1K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Virginia

      Cost and Economics in Pricing Strategy

      Skills you'll gain: Price Negotiation, Market Dynamics, Product Strategy, Revenue Management, Cost Accounting, Economics, Demand Planning, Cost Benefit Analysis, Consumer Behaviour, Marketing Channel, Customer Analysis, Regression Analysis, Competitive Analysis, Statistical Methods

      4.8
      Rating, 4.8 out of 5 stars
      ·
      671 reviews

      Beginner · Course · 1 - 4 Weeks

    Regression learners also search

    Regression Analysis
    Regression Models
    Linear Regression
    Logistic Regression
    Predictive Modeling
    Statistical Modeling
    Predictive Analytics
    Data Modeling
    1…111213…45

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

    • Introduction to Trading, Machine Learning & GCP: Google Cloud
    • Practical Machine Learning: Johns Hopkins University
    • Bayesian Statistics: Duke University
    • Introduction to Predictive Modeling: University of Minnesota
    • Market Research: Queen Mary University of London
    • A Crash Course in Causality: Inferring Causal Effects from Observational Data: University of Pennsylvania
    • Machine Learning and Reinforcement Learning in Finance: New York University
    • Six Sigma Advanced Define and Measure Phases: Kennesaw State University
    • Machine Learning for Data Analysis: Wesleyan University
    • AI for Medical Prognosis : DeepLearning.AI

    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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