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Understanding and Applying Logistic Regression
Vitthal Srinivasan
Vitthal Srinivasan
This course will teach you both the theory and implementation of logistic regression, in Excel (using solver), Python, and R.
Read more
This course will teach you both the theory and implementation of logistic regression, in Excel (using solver), Python, and R.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/j0n4zv/understanding
What's inside
Syllabus
Course Overview
Modeling Relationships Between Variables Using Regression
Understanding Logistic Regression Models
Implementing Logistic Regression Models in Excel
Read more
Syllabus
Course Overview
Modeling Relationships Between Variables Using Regression
Understanding Logistic Regression Models
Implementing Logistic Regression Models in Excel
Read more
Implementing Logistic Regression Models in R
Implementing Logistic Regression Models in Python
Good to know
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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Understanding and Applying Logistic Regression.
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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 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.
Business 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.
Statistician
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.
Market Researcher
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.
Financial Analyst
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.
Operations Research 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.
Credit 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.
Risk 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.
Actuary
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.
Data Scientist
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.
Biostatistician
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.
Epidemiologist
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.
Public Health Analyst
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.
Health Educator
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.
Social Worker
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.
For more career information including salaries, visit:
OpenCourser.com/course/j0n4zv/understanding
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.
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.
Save
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.
Provides a comprehensive overview of statistical learning, including logistic regression. It valuable resource for anyone who wants to learn more about statistical learning, or for those who need a reference book on the topic.
Provides a comprehensive overview of statistical learning, including logistic regression. It valuable resource for anyone who wants to learn more about statistical learning, or for those who need a reference book on the topic.
Provides a comprehensive overview of statistical methods for data analysis, including logistic regression. It valuable resource for anyone who wants to learn more about statistical methods, or for those who need a reference book on the topic.
Save
Provides a comprehensive overview of data mining, including logistic regression. It valuable resource for anyone who wants to learn more about data mining, or for those who need a reference book on the topic.
Provides a comprehensive overview of artificial intelligence, including machine learning. It valuable resource for anyone who wants to learn more about artificial intelligence, or for those who need a reference book on the topic.
Save
Provides a practical guide to using Python for machine learning, including logistic regression. It covers a wide range of topics, including data preparation, model building, and evaluation. It valuable resource for anyone who wants to use Python for machine learning.
Provides a practical guide to using R for machine learning, including logistic regression. It covers a wide range of topics, including data preparation, model building, and evaluation. It valuable resource for anyone who wants to use R for machine learning.
Provides a practical guide to using Python for machine learning, including logistic regression. It covers a wide range of topics, including data preparation, model building, and evaluation. It valuable resource for anyone who wants to use Python for machine learning.
Provides a practical guide to using Python for machine learning, including logistic regression. It covers a wide range of topics, including data preparation, model building, and evaluation. It valuable resource for anyone who wants to use Python for machine learning.
Provides a practical guide to using reinforcement learning, which subfield of machine learning that uses rewards and punishments to train agents to learn how to behave in an environment. It covers a wide range of topics, including reinforcement learning algorithms, exploration, and exploitation. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a practical guide to using Python for deep learning, which subfield of machine learning that uses neural networks to learn from data. It covers a wide range of topics, including neural network architectures, training, and evaluation. It valuable resource for anyone who wants to learn more about deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j0n4zv/understanding
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Time
142 hours
Level
Intermediate
Via
Pluralsight
Instructor
Vitthal Srinivasan
Language
English
Good to know
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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