University of Michigan
Skills you'll gain: Logistic Regression, Model Evaluation, Statistical Inference, Predictive Analytics, R Programming, Predictive Modeling, Probability & Statistics, Statistical Modeling, Statistical Methods, Biostatistics, Regression Analysis, Statistical Analysis, Statistical Hypothesis Testing, Data Analysis, Epidemiology, Descriptive Statistics
Intermediate · Course · 1 - 4 Weeks

Imperial College London
Skills you'll gain: Logistic Regression, Descriptive Statistics, Exploratory Data Analysis, Regression Analysis, Model Evaluation, Statistical Methods, R Programming, Statistical Modeling, Predictive Modeling, Statistical Analysis, Biostatistics, Statistical Software, Predictive Analytics, Probability & Statistics, R (Software), Data Preprocessing, Public Health
★ 4.8 (368) · Intermediate · Course · 1 - 4 Weeks

Johns Hopkins University
Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Logistic Regression, Data Science, Data Analysis, Statistical Methods, Model Evaluation, Predictive Modeling, Probability & Statistics, Statistical Inference, Statistical Hypothesis Testing, Probability Distribution
★ 4.4 (3.4K) · Mixed · Course · 1 - 4 Weeks

Skills you'll gain: Matplotlib, Seaborn, Exploratory Data Analysis, Logistic Regression, NumPy, Machine Learning Methods, Jupyter, Scikit Learn (Machine Learning Library), Data Science, Machine Learning, Machine Learning Algorithms, Python Programming
★ 4.5 (396) · Beginner · Guided Project · Less Than 2 Hours

Skills you'll gain: Regression Analysis, Statistical Hypothesis Testing, Logistic Regression, Statistical Analysis, Statistical Methods, Correlation Analysis, Predictive Modeling, Supervised Learning, Predictive Analytics, Statistical Modeling, Machine Learning, Model Evaluation, Variance Analysis, Python Programming
★ 4.7 (592) · Advanced · Course · 1 - 3 Months

Skills you'll gain: Model Evaluation, Logistic Regression, SAS (Software), Predictive Modeling, Regression Analysis, Predictive Analytics, Feature Engineering, Analytics, Statistical Methods, Data Transformation, Statistical Modeling, Statistical Analysis, Business Analytics, Statistical Programming, Estimation, Probability & Statistics, Probability
Mixed · Course · 1 - 4 Weeks

Skills you'll gain: Descriptive Statistics, A/B Testing, Classification And Regression Tree (CART), Dashboard, Dashboard Creation, Model Evaluation, Model Deployment, Data-Driven Decision-Making, Risk Analysis, Histogram, Statistical Inference, Descriptive Analytics, Simulations, Predictive Modeling, Regression Analysis, Data Visualization, MLOps (Machine Learning Operations), Decision Making, Decision Tree Learning, Keras (Neural Network Library)
Intermediate · Specialization · 3 - 6 Months

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Model Training, Applied Machine Learning, Machine Learning Algorithms, Transfer Learning, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Model Evaluation, Responsible AI, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms
★ 4.9 (39K) · Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Methods, Applied Machine Learning, Model Training, Statistical Machine Learning, Predictive Modeling, Machine Learning Algorithms, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Model Optimization, Predictive Analytics, Classification Algorithms
★ 4.7 (18K) · Intermediate · Course · 1 - 3 Months

Skills you'll gain: Machine Learning Methods, Predictive Modeling, Model Training, Statistical Machine Learning, Machine Learning Software, Model Optimization, Performance Analysis, Performance Metric, Performance Improvement
Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Model Evaluation, Scikit Learn (Machine Learning Library), Data Visualization, Feature Engineering, Data Preprocessing, Model Training, Customer Analysis, Predictive Modeling, Scientific Visualization, Predictive Analytics, Marketing Analytics, Applied Machine Learning, Online Advertising, Data-Driven Marketing, Logistic Regression, Data Cleansing, Data Manipulation, Machine Learning, Python Programming, Deep Learning
★ 4.6 (10) · Beginner · Guided Project · Less Than 2 Hours

Imperial College London
Skills you'll gain: Logistic Regression, Analytical Skills, Correlation Analysis, Science and Research, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Analytics, Descriptive Statistics, Statistical Software, Statistical Modeling, Biostatistics, Exploratory Data Analysis, Model Evaluation, Statistical Analysis, Statistical Programming, R (Software)
★ 4.7 (2.1K) · Beginner · Specialization · 3 - 6 Months
Logistic regression is a statistical method used for binary classification, which means it helps predict the outcome of a dependent variable based on one or more independent variables. It is particularly important because it allows businesses and researchers to understand relationships between variables and make informed decisions based on data. For instance, logistic regression can be used to predict whether a customer will purchase a product or not, based on their demographic information and past behavior.‎
With skills in logistic regression, you can pursue various roles in data analysis, statistics, and machine learning. Common job titles include Data Analyst, Data Scientist, Statistician, and Business Analyst. These positions often require the ability to interpret complex data sets and provide actionable insights, making logistic regression a valuable skill in many industries, including healthcare, finance, and marketing.‎
To effectively learn logistic regression, you should focus on developing a strong foundation in statistics and data analysis. Key skills include understanding probability, familiarity with statistical software (like R or Python), and the ability to interpret model outputs. Additionally, knowledge of data preprocessing techniques and experience with data visualization can enhance your ability to communicate findings effectively.‎
There are several excellent online courses available for learning logistic regression. For instance, you might consider Logistic Regression Fundamentals: Analyze & Predict for a comprehensive introduction. Additionally, courses like Logistic Regression and Prediction for Health Data and Python: Logistic Regression & Supervised ML offer specialized insights into applying logistic regression in different contexts.‎
Yes. You can start learning logistic regression on Coursera for free in two ways:
If you want to keep learning, earn a certificate in logistic regression, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
To learn logistic regression, start by enrolling in an online course that fits your learning style. Engage with the course materials, complete exercises, and practice coding if applicable. Additionally, consider working on real-world projects or datasets to apply what you've learned. Joining online forums or study groups can also provide support and enhance your understanding.‎
Typical topics covered in logistic regression courses include the fundamentals of logistic regression, model fitting, interpretation of coefficients, evaluation metrics (like accuracy and ROC curves), and practical applications in various fields. Some courses may also explore advanced topics such as regularization techniques and the use of logistic regression in machine learning frameworks.‎
For training and upskilling employees, courses like Logistic Regression with SAS: Build & Evaluate Models and SPSS: Apply & Interpret Logistic Regression Models are particularly beneficial. These courses provide practical skills that can be directly applied in the workplace, helping teams leverage data for better decision-making.‎