Logistic regression courses can help you learn statistical modeling, hypothesis testing, and the interpretation of coefficients. You can build skills in evaluating model performance, understanding odds ratios, and applying techniques like regularization to improve accuracy. Many courses introduce tools such as R, Python, and specialized libraries like scikit-learn, showing how these skills are used to analyze binary outcomes in various fields, including healthcare, finance, and marketing.

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

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

Skills you'll gain: Matplotlib, Data Visualization, Seaborn, Logistic Regression, NumPy, Data Analysis, Jupyter, Data Science, Machine Learning, Machine Learning Algorithms, Python Programming, Supervised Learning, Classification Algorithms, Algorithms
Beginner · Guided Project · Less Than 2 Hours

Multiple educators
Skills you'll gain: Unsupervised Learning, Supervised Learning, Transfer Learning, Machine Learning, Jupyter, Applied Machine Learning, Data Ethics, Decision Tree Learning, Model Evaluation, Tensorflow, Scikit Learn (Machine Learning Library), NumPy, Predictive Modeling, Deep Learning, Artificial Intelligence, Classification Algorithms, Reinforcement Learning, Random Forest Algorithm, Feature Engineering, Data Preprocessing
Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Regression Analysis, Logistic Regression, Statistical Hypothesis Testing, Data Analysis, Advanced Analytics, Statistical Analysis, Correlation Analysis, Analytical Skills, Business Analytics, Statistical Modeling, Model Evaluation, Variance Analysis, Predictive Modeling, Machine Learning, Python Programming
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, Estimation, Probability
Mixed · Course · 1 - 4 Weeks

Skills you'll gain: Unsupervised Learning, Supervised Learning, Model Evaluation, Regression Analysis, Scikit Learn (Machine Learning Library), Applied Machine Learning, Predictive Modeling, Machine Learning, Dimensionality Reduction, Decision Tree Learning, Python Programming, Logistic Regression, Classification Algorithms, Feature Engineering
Intermediate · Course · 1 - 3 Months
Duke University
Skills you'll gain: Data-Driven Decision-Making, Logistic Regression, Statistical Modeling, Model Evaluation, Predictive Modeling, Regression Analysis, R Programming, Statistics, Data Analysis, Probability & Statistics, Statistical Inference
Beginner · Course · 1 - 4 Weeks

Skills you'll gain: Data Cleansing, Logistic Regression, Data Preprocessing, Applied Machine Learning, Data Import/Export, Data Mining, Python Programming, Google Cloud Platform, Scikit Learn (Machine Learning Library), Predictive Modeling, Classification Algorithms, Machine Learning, Supervised Learning
Intermediate · Guided Project · Less Than 2 Hours

DeepLearning.AI
Skills you'll gain: Supervised Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Classification Algorithms, Feature Engineering, Artificial Intelligence, Model Evaluation, Data Preprocessing, Python Programming, Logistic Regression, Regression Analysis, Unsupervised Learning
Beginner · Course · 1 - 4 Weeks

Johns Hopkins University
Skills you'll gain: Biostatistics, Regression Analysis, Logistic Regression, Statistical Methods, Public Health, Probability & Statistics, Statistical Analysis, Statistical Inference, Advanced Analytics, Statistical Modeling, Predictive Modeling, Model Evaluation
Beginner · Course · 1 - 4 Weeks
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.‎