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Understanding and Applying Logistic Regression

This course will teach you both the theory and implementation of logistic regression, in Excel (using solver), Python, and R.

Logistic Regression is a great tool for two common applications: binary classification, and attributing cause-effect relationships where the response is a categorical variable. While the first links logistic regression to other classification algorithms (such as Naive Bayes), the second is a natural extension of Linear Regression. In this course, Understanding and Applying Logistic Regression, you'll get a better understanding of logistic regression and how to apply it. First, you'll discover applications of logistic regression and how logistic regression is linked to linear regression and machine learning. Next, you'll explore the s-curve and its standard mathematical form. Finally, you'll learn whether Google's stock returns will go up or down, using Excel (solver), R, and Python. By the end of this course, you'll have a strong applied knowledge of logistic regression that will help you solve complex business problems.

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What's inside

Syllabus

Course Overview

Modeling Relationships Between Variables Using Regression

Understanding Logistic Regression Models

Implementing Logistic Regression Models in Excel

Implementing Logistic Regression Models in R

Implementing Logistic Regression Models in Python

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a deep understanding of Logistic Regression, which is highly relevant to fields such as predictive analytics and binary classification

Taught by Vitthal Srinivasan, a recognized expert in the field of data science and analytics

Provides practical implementation exercises in Excel, Python, and R, enhancing your programming skills

Suitable for learners with a basic understanding of statistics and machine learning concepts

May be less beneficial for those seeking an exhaustive study of Logistic Regression

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Understanding and Applying Logistic Regression with these activities:

Review probability and statistics concepts

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Logistic regression is based on probability theory and statistics. Reviewing these concepts will help you better understand the theoretical foundations of logistic regression.

Browse courses on Probability

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Review your notes or textbooks on probability and statistics
Solve some practice problems on probability and statistics

Review linear regression concepts

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Logistic regression is an extension of linear regression. Reviewing the concepts of linear regression will help you better understand logistic regression.

Browse courses on Linear Regression

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Review your notes or textbooks on linear regression
Solve some practice problems on linear regression

Participate in peer-led study groups

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Promotes active engagement and deeper understanding through collaborative learning with peers.

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Identify or form a study group with classmates
Set regular meeting times and create a study schedule
Take turns leading discussions, presenting concepts, and solving problems together
Provide constructive feedback and support to fellow peers

Nine other activities

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Follow tutorials on logistic regression

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Tutorials can provide a structured approach to learning logistic regression and demonstrate its practical applications.

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Find tutorials on platforms like Coursera, edX, or YouTube
Follow the tutorials and complete the exercises

Solve logistic regression practice problems

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Provides additional practice and improves problem-solving skills related to logistic regression.

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Review the basic concepts of logistic regression
Attempt a variety of practice problems covering different scenarios and datasets
Analyze your answers and identify areas where further improvement is needed

Read Elements of Statistical Learning (2nd Edition)

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This book provides a comprehensive overview of logistic regression and other statistical modeling techniques that will deepen your understanding of the course concepts.

View An Introduction to Statistical Learning on Amazon

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Read chapters 4-6 (Classification)
Complete the exercises at the end of each chapter

Develop an infographic on the applications of logistic regression

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Enhances understanding and retention of logistic regression applications through visual representation.

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Gather information and data on different applications of logistic regression
Design an infographic that visually represents the information in a clear and engaging manner
Use appropriate visuals, charts, and text to convey the key concepts effectively

Solve practice problems on logistic regression

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Solving practice problems will reinforce your understanding of the concepts and techniques covered in the course.

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Find practice problems online or in textbooks
Solve the problems and check your answers

Form a study group with other course participants

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Studying with peers can help you reinforce concepts, improve your understanding, and identify areas where you need additional support.

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Find other course participants who are interested in forming a study group
Meet regularly to discuss course material, solve problems, and work on projects

Implement multiple logistic regression models with different inputs

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Helps to improve understanding and application of logistic regression through focused practice.

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Identify a dataset suitable for logistic regression analysis
Define the input features and target variable
Build and train logistic regression models with different combinations of features
Evaluate the accuracy and performance of each model using metrics like accuracy, precision, and recall
Analyze the results and compare the performance of different models

Attend a workshop on logistic regression

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Attending a workshop can provide you with an immersive and hands-on learning experience, allowing you to deepen your understanding of logistic regression and its applications.

Browse courses on Logistic Regression

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Find a workshop on logistic regression
Attend the workshop and actively participate in the activities

Build a logistic regression model to predict a business outcome

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Building a real-world model will allow you to apply the concepts and techniques you learn in the course to a practical problem.

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Identify a business problem that can be solved using logistic regression
Collect and prepare data
Build and evaluate a logistic regression model
Deploy the model and track its performance

Career center

Learners who complete Understanding and Applying Logistic Regression will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data analysts collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations for businesses on how to improve their operations. Logistic regression is a statistical technique that is often used in data analysis. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of an event occurring. This skill can be valuable for data analysts who want to work in fields such as finance, healthcare, and marketing.

See salaries and explore the career path for Data Analyst

Business Analyst

Business analysts help businesses identify and solve problems. They use their analytical skills to evaluate data, identify trends, and make recommendations for improvements. Logistic regression is a statistical technique that can be used to identify relationships between variables. By taking this course, you will learn how to use logistic regression to build models that can predict the likelihood of a particular outcome. This skill can be valuable for business analysts who want to work in fields such as consulting, finance, and operations.

See salaries and explore the career path for Business Analyst

Statistician

Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They work in a variety of fields, including finance, healthcare, and marketing. Logistic regression is a statistical technique that is often used to predict the probability of an event occurring. By taking this course, you will learn how to use logistic regression to build models that can make predictions. This skill can be valuable for statisticians who want to work in fields such as data analysis, research, and consulting.

See salaries and explore the career path for Statistician

Market Researcher

Market researchers collect and analyze data about consumer behavior. They use their findings to help businesses understand their customers and make better marketing decisions. Logistic regression is a statistical technique that can be used to identify relationships between variables. By taking this course, you will learn how to use logistic regression to build models that can predict the likelihood of a customer making a purchase. This skill can be valuable for market researchers who want to work in fields such as marketing, advertising, and sales.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial analysts evaluate the financial performance of companies and make recommendations for investments. They use a variety of financial data to identify trends and make predictions. Logistic regression is a statistical technique that can be used to predict the probability of an event occurring. By taking this course, you will learn how to use logistic regression to build models that can predict the likelihood of a company's stock price going up or down. This skill can be valuable for financial analysts who want to work in fields such as investment banking, asset management, and hedge funds.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations research analysts use mathematical and statistical techniques to solve business problems. They work in a variety of industries, including manufacturing, transportation, and healthcare. Logistic regression is a statistical technique that can be used to identify relationships between variables. By taking this course, you will learn how to use logistic regression to build models that can help businesses make better decisions. This skill can be valuable for operations research analysts who want to work in fields such as supply chain management, logistics, and scheduling.

See salaries and explore the career path for Operations Research Analyst

Credit Analyst

Credit analysts evaluate the creditworthiness of borrowers. They use a variety of financial data to determine the likelihood that a borrower will repay a loan. Logistic regression is a statistical technique that can be used to predict the probability of an event occurring. By taking this course, you will learn how to use logistic regression to build models that can predict the likelihood of a borrower defaulting on a loan. This skill can be valuable for credit analysts who want to work in fields such as banking, lending, and insurance.

See salaries and explore the career path for Credit Analyst

Risk Analyst

Risk analysts evaluate the risks faced by businesses and make recommendations for how to mitigate those risks. They use a variety of data to identify and assess risks. Logistic regression is a statistical technique that can be used to predict the probability of an event occurring. By taking this course, you will learn how to use logistic regression to build models that can predict the likelihood of a risk event occurring. This skill can be valuable for risk analysts who want to work in fields such as insurance, finance, and healthcare.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of fields, including insurance, pensions, and finance. Logistic regression is a statistical technique that can be used to predict the probability of an event occurring. By taking this course, you will learn how to use logistic regression to build models that can predict the likelihood of an insurance claim being filed or a pension plan being underfunded. This skill can be valuable for actuaries who want to work in fields such as insurance, pensions, and finance.

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Data Scientist

Data scientists use a variety of mathematical, statistical, and programming techniques to extract knowledge from data. They work in a variety of fields, including healthcare, finance, and marketing. Logistic regression is a statistical technique that is often used in data science. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of an event occurring. This skill can be valuable for data scientists who want to work in fields such as data mining, machine learning, and artificial intelligence.

See salaries and explore the career path for Data Scientist

Biostatistician

Biostatisticians use statistical methods to design and analyze studies in the field of medicine. They work with doctors and other healthcare professionals to collect and analyze data on patients. Logistic regression is a statistical technique that is often used in biostatistics. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of a patient developing a disease or responding to a treatment. This skill can be valuable for biostatisticians who want to work in fields such as clinical research, epidemiology, and public health.

See salaries and explore the career path for Biostatistician

Epidemiologist

Epidemiologists investigate the causes of disease and develop strategies to prevent and control it. They work with public health officials and other healthcare professionals to collect and analyze data on the incidence and prevalence of diseases. Logistic regression is a statistical technique that is often used in epidemiology. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of a person developing a disease. This skill can be valuable for epidemiologists who want to work in fields such as public health, infectious disease, and chronic disease.

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Public Health Analyst

Public health analysts use data to identify and address public health problems. They work with public health officials and other healthcare professionals to collect and analyze data on the health of communities. Logistic regression is a statistical technique that is often used in public health. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of a person developing a disease or being exposed to a hazard. This skill can be valuable for public health analysts who want to work in fields such as environmental health, maternal and child health, and disease prevention.

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Health Educator

Health educators develop and implement educational programs to promote health and prevent disease. They work with individuals, groups, and communities to teach people about health risks and behaviors. Logistic regression is a statistical technique that can be used to identify factors that influence health behaviors. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of a person engaging in a healthy behavior or developing a disease. This skill can be valuable for health educators who want to work in fields such as chronic disease prevention, tobacco control, and sexual health.

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Social Worker

Social workers help people cope with personal and social problems. They work with individuals, families, and communities to provide support and resources. Logistic regression is a statistical technique that can be used to identify factors that influence social problems. By taking this course, you will learn how to use logistic regression to build models that can predict the probability of a person experiencing a social problem or needing social services. This skill can be valuable for social workers who want to work in fields such as child welfare, mental health, and aging.

See salaries and explore the career path for Social Worker

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Understanding and Applying Logistic Regression.

Applied Logistic Regression

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Provides a comprehensive overview of logistic regression, covering both the theory and practice of the technique. It valuable resource for anyone who wants to learn more about logistic regression, or for those who need a reference book on the topic.

Applied Logistic Regression

Hardcover

$$$

Economic Growth

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Provides a practical guide to using logistic regression for data analysis. It covers a wide range of topics, including model building, interpretation, and validation. It valuable resource for anyone who wants to use logistic regression for real-world problems.

Understanding and Applying Logistic Regression

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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