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Dennis Davenport
and
MOUSSA DOUMBIA
In this course, you'll learn how to distinguish between the different types of regression models. You will apply the Method of Least Squares to a dataset by hand and using Python. In addition, you will learn how to employ a linear regression model to identify scenarios. Let's get started!
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What's inside
Syllabus
Introduction to Regression Models
In module 1, you’ll learn how to define regression and learn about the various types of regression models and how they are used. We will cover the following learning objectives.
Read more
Syllabus
Introduction to Regression Models
In module 1, you’ll learn how to define regression and learn about the various types of regression models and how they are used. We will cover the following learning objectives.
Read more
Using the Method of Least Squares
Let’s recap! In module 1, you learned how to define regression models and use the various types of regression models. In module 2, you’ll gain the knowledge you need to know in order to apply the method of least squares.. You’ll also learn how to apply the method of least squares using Python. We will cover the following learning objectives.
Using Linear Regression Models
Let’s recap! In module 2, you learned how to apply the method of least squares. In module 3, you will learn how to understand linear regression models. We will cover the following learning objectives.
Using Linear Regression Model
Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: regression, regression models, applying the method of least squares and, understanding linear regression models. In the final module of the course, you’ll apply what you’ve learned to concrete, real-world examples. You’ll review real-world linear regression models and complete peer reviews. We will cover the following learning objectives.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Meant for beginners, this course will help learners understand the fundamentals of regression models
Teaches the method of least squares, which is practical in fields like machine learning and statistics
Involves real-world examples and peer reviews, enhancing practical application
Applies Python, commonly used in data analysis and machine learning
Provides a basis for learners who wish to pursue further studies in regression analysis
Taught by Dennis Davenport, a seasoned instructor with experience in data science
Save this course
Save Building Regression Models with Linear Algebra to your list so you can find it easily later:
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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 Building Regression Models with Linear Algebra with these
activities:
An Introduction to Statistical Learning
Show steps
Expand your knowledge of regression models and statistical learning through a comprehensive textbook.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read through the chapters on regression analysis, including linear regression, logistic regression, and tree-based models.
-
Work through the practice exercises and examples provided in the book.
-
Apply the concepts to real-world datasets to reinforce your understanding.
Linear algebra exercises
Show steps
Sharpen your linear algebra skills to enhance your understanding of regression models.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of matrices, vectors, and linear transformations.
-
Practice solving systems of linear equations using various methods.
-
Calculate determinants and eigenvalues of matrices.
-
Complete online exercises or worksheets to test your understanding.
Review R programming
Show steps
Review concepts of a related programming language to prepare for the course.
Browse courses on
Python
Show steps
-
Review the basics of R programming, including data types, operators, and control flow.
-
Practice writing simple R scripts to perform data manipulation tasks.
-
Complete online tutorials or exercises to reinforce your understanding of R programming.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Kaggle notebook tutorials
Show steps
Further explore linear regression concepts and applications through guided Kaggle tutorials.
Browse courses on
Linear Regression
Show steps
-
Browse through the Kaggle website for notebooks related to linear regression.
-
Choose a notebook that aligns with your interests or learning goals.
-
Follow the step-by-step instructions in the notebook to implement linear regression models.
-
Analyze the results and interpret the coefficients of the model.
Online discussion forum
Show steps
Engage with peers to discuss concepts, share insights, and clarify any doubts related to regression models.
Browse courses on
Online Discussion
Show steps
-
Identify an online discussion forum or platform relevant to the course.
-
Participate in discussions by asking questions, sharing your perspectives, and responding to others.
-
Collaborate with peers to solve problems and gain a deeper understanding of the material.
Regression Analysis Workshop
Show steps
Attend a workshop to gain hands-on experience with regression analysis techniques and tools.
Browse courses on
Regression Analysis
Show steps
-
Research and identify a workshop that aligns with your learning goals.
-
Register for the workshop and actively participate in the sessions.
-
Practice the regression analysis techniques covered in the workshop.
Data visualization project
Show steps
Apply your regression analysis skills to create informative data visualizations for real-world scenarios.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that you are interested in analyzing.
-
Perform linear regression analysis on the dataset to identify trends and relationships.
-
Use a data visualization tool like Tableau or Power BI to create interactive dashboards or charts.
-
Present your findings and insights based on the data visualizations.
Contribute to Open Source Regression Projects
Show steps
Immerse yourself in the practical aspects of regression analysis by contributing to open source projects.
Browse courses on
Open Source
Show steps
-
Identify open source regression projects on platforms like GitHub or GitLab.
-
Review the project documentation and codebase to understand the project's goals and implementation.
-
Identify areas where you can contribute your skills, such as bug fixes, feature improvements, or documentation updates.
-
Submit pull requests to the project and collaborate with the community.
An Introduction to Statistical Learning
Show steps
Expand your knowledge of regression models and statistical learning through a comprehensive textbook.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read through the chapters on regression analysis, including linear regression, logistic regression, and tree-based models.
- Work through the practice exercises and examples provided in the book.
- Apply the concepts to real-world datasets to reinforce your understanding.
Linear algebra exercises
Show steps
Sharpen your linear algebra skills to enhance your understanding of regression models.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of matrices, vectors, and linear transformations.
- Practice solving systems of linear equations using various methods.
- Calculate determinants and eigenvalues of matrices.
- Complete online exercises or worksheets to test your understanding.
Review R programming
Show steps
Review concepts of a related programming language to prepare for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of R programming, including data types, operators, and control flow.
- Practice writing simple R scripts to perform data manipulation tasks.
- Complete online tutorials or exercises to reinforce your understanding of R programming.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Kaggle notebook tutorials
Show steps
Further explore linear regression concepts and applications through guided Kaggle tutorials.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Browse through the Kaggle website for notebooks related to linear regression.
- Choose a notebook that aligns with your interests or learning goals.
- Follow the step-by-step instructions in the notebook to implement linear regression models.
- Analyze the results and interpret the coefficients of the model.
Online discussion forum
Show steps
Engage with peers to discuss concepts, share insights, and clarify any doubts related to regression models.
Browse courses on
Online Discussion
Show steps
Show steps
Show steps
- Identify an online discussion forum or platform relevant to the course.
- Participate in discussions by asking questions, sharing your perspectives, and responding to others.
- Collaborate with peers to solve problems and gain a deeper understanding of the material.
Regression Analysis Workshop
Show steps
Attend a workshop to gain hands-on experience with regression analysis techniques and tools.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Research and identify a workshop that aligns with your learning goals.
- Register for the workshop and actively participate in the sessions.
- Practice the regression analysis techniques covered in the workshop.
Data visualization project
Show steps
Apply your regression analysis skills to create informative data visualizations for real-world scenarios.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that you are interested in analyzing.
- Perform linear regression analysis on the dataset to identify trends and relationships.
- Use a data visualization tool like Tableau or Power BI to create interactive dashboards or charts.
- Present your findings and insights based on the data visualizations.
Contribute to Open Source Regression Projects
Show steps
Immerse yourself in the practical aspects of regression analysis by contributing to open source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open source regression projects on platforms like GitHub or GitLab.
- Review the project documentation and codebase to understand the project's goals and implementation.
- Identify areas where you can contribute your skills, such as bug fixes, feature improvements, or documentation updates.
- Submit pull requests to the project and collaborate with the community.
Career center
Learners who complete Building Regression Models with Linear Algebra will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians collect, analyze, and interpret data to derive meaningful insights. Regression models are a cornerstone of statistical analysis, enabling you to make inferences and predictions from data. Completing "Building Regression Models with Linear Algebra" will deepen your understanding of statistical modeling and prepare you for a rewarding career as a statistician.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design and implement machine learning models to solve real-world problems. Regression models are fundamental in machine learning for tasks such as prediction and anomaly detection. Taking "Building Regression Models with Linear Algebra" will provide you with a deep understanding of regression techniques, enabling you to excel as a machine learning engineer.
Data Scientist
Data Scientist
Data scientists leverage statistical and mathematical techniques to extract insights from data, addressing complex business challenges. Regression models are widely used in data science for prediction, classification, and forecasting. By completing this course, you will gain a solid foundation in regression modeling, enhancing your capabilities as a data scientist.
Quantitative Trader
Quantitative Trader
Quantitative traders use mathematical and statistical models to make trading decisions. Regression models are vital for analyzing financial data and identifying trading opportunities. By taking "Building Regression Models with Linear Algebra," you will gain valuable insights into the techniques used by successful quantitative traders.
Data Analyst
Data Analyst
Data analysts leverage mathematical and statistical techniques to extract insights from data, driving decision-making. The course will enhance your understanding of regression models, which are widely used for data analysis. By applying these models, you can gain valuable skills for a successful career as a data analyst.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to medical data, providing insights for clinical research and healthcare decision-making. Regression models are essential for analyzing clinical trials and predicting disease outcomes. This course will equip you with the statistical knowledge and skills to succeed as a biostatistician.
Financial Modeler
Financial Modeler
Financial modelers create financial models to assess investment opportunities and make informed financial decisions. Regression models are widely used in financial modeling for forecasting and scenario analysis. Taking "Building Regression Models with Linear Algebra" will provide you with a strong foundation in regression techniques, enabling you to succeed as a financial modeler.
Actuary
Actuary
Actuaries use mathematical and statistical skills to analyze risk and uncertainty, making them essential for insurance companies, consulting firms, and financial institutions. Taking "Building Regression Models with Linear Algebra" will help build a foundation in statistical modeling, enabling you to effectively assess and manage risk.
Economist
Economist
Economists analyze economic data and develop models to understand economic trends. Regression models are widely used in economics for forecasting and policy analysis. By taking this course, you will gain valuable insights into the use of regression models in economic modeling, enhancing your effectiveness as an economist.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical models to optimize business processes and solve complex problems. Linear regression is a fundamental tool for analyzing data and making reliable predictions. This course will provide you with the necessary foundation to excel as an operations research analyst.
Financial Analyst
Financial Analyst
Financial analysts play a crucial role in evaluating investments and providing financial advice. Regression models are essential for forecasting financial trends and making investment decisions. Completing "Building Regression Models with Linear Algebra" will equip you with the knowledge and skills to succeed as a financial analyst.
Risk Analyst
Risk Analyst
Risk analysts assess and manage financial and operational risks for businesses. Understanding regression models is essential for quantifying risk and developing mitigation strategies. This course will enhance your knowledge of statistical modeling and its application in risk management.
Underwriter
Underwriter
Underwriters assess and price risks for insurance policies. Regression models are essential for predicting the probability of an event occurring. Taking "Building Regression Models with Linear Algebra" can provide you with the statistical knowledge and skills to make informed underwriting decisions.
Market Researcher
Market Researcher
Market researchers analyze consumer behavior and trends to inform marketing strategies. Understanding regression models is invaluable in this field, as they allow you to quantify relationships between variables and make accurate predictions. Taking this course will enhance your expertise in market research.
Teacher
Teacher
Teachers who instruct in Mathematics or Statistics can benefit from "Building Regression Models with Linear Algebra." It provides a solid understanding of regression models, which are widely used in various fields, including economics, finance, and data science. By enhancing your knowledge, you can effectively teach students about this important statistical technique.
For more career information including salaries, visit:
OpenCourser.com/course/chygra/building
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
Building Regression Models with Linear Algebra.
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, and support vector machines. It valuable resource for anyone who wants to learn more about machine learning.
More advanced treatment of statistical learning methods than Introduction to Statistical Learning. It covers a wider range of topics, including Bayesian methods, nonparametric methods, and time series analysis.
Practical guide to machine learning using Python. It covers a wide range of topics, including data preprocessing, model training, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning.
Practical guide to applied linear regression. It covers a wide range of topics, including model selection, diagnostic checking, and variable selection.
Provides an overview of regression modeling with a focus on actuarial and financial applications. It covers a wide range of topics, including generalized linear models, mixed models, and Bayesian models.
Practical guide to regression analysis. It covers a wide range of topics, including simple linear regression, multiple linear regression, and logistic regression.
Provides a comprehensive overview of linear algebra. It covers a wide range of topics, including vectors, matrices, and linear transformations. It valuable resource for anyone who wants to learn more about linear algebra.
Provides an overview of matrix analysis with a focus on statistical applications. It covers a wide range of topics, including matrix algebra, linear models, and multivariate analysis.
Provides an overview of applied linear algebra with a focus on optimization and machine learning. It covers a wide range of topics, including matrix algebra, linear programming, and convex optimization.
Save
Provides a comprehensive overview of matrix computations. It covers a wide range of topics, including matrix algebra, numerical linear algebra, and parallel computing.
Provides a comprehensive overview of numerical linear algebra. It covers a wide range of topics, including matrix algebra, numerical linear algebra, and parallel computing.
Provides an overview of statistical methods used in the atmospheric sciences. It covers a wide range of topics, including linear regression, logistic regression, and time series analysis.
Provides an overview of statistical methods used in agricultural, biological, and environmental sciences. It covers a wide range of topics, including linear regression, logistic regression, and time series analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/chygra/building
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Level
Introductory
Via
Coursera
Institution
Howard University
Instructors
Dennis Davenport
MOUSSA DOUMBIA
Language
English
This course belongs to a
collection
called
Linear Algebra for Data Science Using Python. It's comprised of four courses:
- Fundamental Linear Algebra Concepts with Python
- Introduction to Linear Algebra and Python
- Capstone: Data Science Problem in Linear Algebra Framework
- Building Regression Models with Linear Algebra (viewing)
Good to know
Meant for beginners, this course will help learners understand the fundamentals of regression models
Teaches the method of least squares, which is practical in fields like machine learning and statistics
Involves real-world examples and peer reviews, enhancing practical application
Applies Python, commonly used in data analysis and machine learning
Provides a basis for learners who wish to pursue further studies in regression analysis
Taught by Dennis Davenport, a seasoned instructor with experience in data science
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