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James Bird
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra.
Read more
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra.
Read more
Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Linear Algebra that you will need for a career in Data Science without a ton of unnecessary proofs and concepts that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Linear Algebra.
This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.
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What's inside
Syllabus
Linear Systems and Gaussian Elimination
In this module we will learn what a matrix is and what it represents. We will explore how a system of linear equations can be expressed in a neat package via matrices. Lastly, we will delve into coordinate systems and provide visualizations to help you understand matrices in a more well-rounded way.
Read more
Syllabus
Linear Systems and Gaussian Elimination
In this module we will learn what a matrix is and what it represents. We will explore how a system of linear equations can be expressed in a neat package via matrices. Lastly, we will delve into coordinate systems and provide visualizations to help you understand matrices in a more well-rounded way.
Read more
Matrix Algebra
In this module we will learn how to solve a linear system of equations with matrix algebra.
Properties of a Linear System
In this module we will explore concepts and properties of linear systems. This includes independence, basis, rank, row space, column space, and much more.
Determinant and Eigens
In this module we will discuss projections and how they work. We will build on a foundation using 2-dimensional projections and explore the concept in higher dimensions over time.
Projections and Least Squares
In this module we will learn how to compute the determinant of a matrix. Afterwards, Eigenvalues and Eigenvectors will be covered.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Targets students lacking a math background, making it accessible to those new to the field
Emphasizes practical applications, ensuring learners can apply their knowledge in real-world scenarios
Provides a solid foundation in Linear Algebra, preparing learners for advanced data science concepts and applications
Facilitates a deeper understanding of Linear Algebra by using approachable methods and concepts
Focuses on key Linear Algebra concepts, avoiding unnecessary theoretical content
Designed to prepare learners for the Statistical Modeling for Data Science Application course in the CU Boulder's Master of Science in Data Science program
Save this course
Save Essential Linear Algebra for Data Science 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 Essential Linear Algebra for Data Science with these
activities:
Review matrix basics
Show steps
Brush up on the basics of matrices, including operations, properties, and applications. This will provide a solid foundation for understanding the more advanced concepts covered in this course.
Browse courses on
Matrices
Show steps
-
Review the definition and properties of matrices.
-
Practice performing basic matrix operations, such as addition, subtraction, and multiplication.
-
Solve systems of linear equations using matrices.
Volunteer as a math tutor
Show steps
Reinforce your understanding of linear algebra by helping others learn the concepts. Volunteering as a math tutor will allow you to explain concepts, solve problems, and make a positive impact on the community.
Browse courses on
Linear Algebra
Show steps
-
Contact local schools or tutoring centers to inquire about volunteer opportunities.
-
Prepare lesson plans and materials based on the curriculum.
-
Assist students with their math problems and provide guidance.
Participate in a study group for linear algebra
Show steps
Collaborate with peers to enhance your understanding of linear algebra. Study groups provide a supportive environment for asking questions, sharing insights, and working through problems together.
Browse courses on
Linear Algebra
Show steps
-
Find or form a study group with classmates or peers.
-
Meet regularly to discuss course material, solve problems, and review concepts.
-
Take turns leading the discussion and presenting solutions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore matrix concepts through tutorials
Show steps
Deepen your understanding of matrix algebra and linear systems by following guided tutorials. This will provide a structured approach to learning and allow you to ask questions and receive feedback.
Browse courses on
Matrix Algebra
Show steps
-
Find tutorials on matrix algebra and linear systems.
-
Follow the tutorials step-by-step, completing any practice exercises.
-
Ask questions and seek clarification when needed.
Create a linear algebra resource guide
Show steps
Organize and share your knowledge of linear algebra by creating a resource guide. This will help you synthesize your understanding and make it accessible to others.
Browse courses on
Linear Algebra
Show steps
-
Gather and organize materials on linear algebra, including textbooks, articles, and online resources.
-
Create a guide that categorizes and summarizes the resources.
-
Share your guide with classmates, peers, or the online community.
Solve practice problems on linear systems
Show steps
Strengthen your problem-solving skills by practicing solving linear systems. This will help you develop fluency and confidence in applying matrix algebra concepts.
Browse courses on
Linear Systems
Show steps
-
Find a collection of practice problems on linear systems.
-
Solve the problems, checking your answers against provided solutions.
-
Identify areas where you need further practice and focus on those topics.
Develop a linear algebra application
Show steps
Apply your knowledge of linear algebra to a practical project. Developing an application will allow you to explore real-world uses of the concepts and gain hands-on experience.
Browse courses on
Linear Algebra
Show steps
-
Identify a problem or need that can be addressed using linear algebra.
-
Design and develop an application that solves the problem.
-
Test and refine your application.
Participate in a math competition
Show steps
Challenge yourself by participating in a math competition that covers linear algebra concepts. This will test your knowledge, encourage critical thinking, and potentially lead to recognition for your achievements.
Browse courses on
Linear Algebra
Show steps
-
Find a math competition that aligns with your skill level and interests.
-
Register for the competition and prepare thoroughly.
-
Complete the competition and receive feedback on your performance.
Review matrix basics
Show steps
Brush up on the basics of matrices, including operations, properties, and applications. This will provide a solid foundation for understanding the more advanced concepts covered in this course.
Browse courses on
Matrices
Show steps
Show steps
Show steps
- Review the definition and properties of matrices.
- Practice performing basic matrix operations, such as addition, subtraction, and multiplication.
- Solve systems of linear equations using matrices.
Volunteer as a math tutor
Show steps
Reinforce your understanding of linear algebra by helping others learn the concepts. Volunteering as a math tutor will allow you to explain concepts, solve problems, and make a positive impact on the community.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Contact local schools or tutoring centers to inquire about volunteer opportunities.
- Prepare lesson plans and materials based on the curriculum.
- Assist students with their math problems and provide guidance.
Participate in a study group for linear algebra
Show steps
Collaborate with peers to enhance your understanding of linear algebra. Study groups provide a supportive environment for asking questions, sharing insights, and working through problems together.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Find or form a study group with classmates or peers.
- Meet regularly to discuss course material, solve problems, and review concepts.
- Take turns leading the discussion and presenting solutions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore matrix concepts through tutorials
Show steps
Deepen your understanding of matrix algebra and linear systems by following guided tutorials. This will provide a structured approach to learning and allow you to ask questions and receive feedback.
Browse courses on
Matrix Algebra
Show steps
Show steps
Show steps
- Find tutorials on matrix algebra and linear systems.
- Follow the tutorials step-by-step, completing any practice exercises.
- Ask questions and seek clarification when needed.
Create a linear algebra resource guide
Show steps
Organize and share your knowledge of linear algebra by creating a resource guide. This will help you synthesize your understanding and make it accessible to others.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Gather and organize materials on linear algebra, including textbooks, articles, and online resources.
- Create a guide that categorizes and summarizes the resources.
- Share your guide with classmates, peers, or the online community.
Solve practice problems on linear systems
Show steps
Strengthen your problem-solving skills by practicing solving linear systems. This will help you develop fluency and confidence in applying matrix algebra concepts.
Browse courses on
Linear Systems
Show steps
Show steps
Show steps
- Find a collection of practice problems on linear systems.
- Solve the problems, checking your answers against provided solutions.
- Identify areas where you need further practice and focus on those topics.
Develop a linear algebra application
Show steps
Apply your knowledge of linear algebra to a practical project. Developing an application will allow you to explore real-world uses of the concepts and gain hands-on experience.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify a problem or need that can be addressed using linear algebra.
- Design and develop an application that solves the problem.
- Test and refine your application.
Participate in a math competition
Show steps
Challenge yourself by participating in a math competition that covers linear algebra concepts. This will test your knowledge, encourage critical thinking, and potentially lead to recognition for your achievements.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Find a math competition that aligns with your skill level and interests.
- Register for the competition and prepare thoroughly.
- Complete the competition and receive feedback on your performance.
Career center
Learners who complete Essential Linear Algebra for Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for building and maintaining analytical models that solve business problems. They use data to identify trends, patterns, and anomalies, and then use this information to make recommendations and predictions. This course can be useful for a Data Scientist because it will help them build a foundation in linear algebra, which is essential for understanding data and building models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work with data scientists to identify the right algorithms and models to solve business problems, and then they build and deploy these models into production. This course can be useful for a Machine Learning Engineer because it will help them build a foundation in linear algebra, which is essential for understanding machine learning algorithms.
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data. They use data to identify trends, patterns, and anomalies, and then they use this information to make recommendations and predictions. This course can be useful for a Data Analyst because it will help them build a foundation in linear algebra, which is essential for understanding data and building models.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical methods to analyze data and make predictions. They work in a variety of industries, including finance, insurance, and healthcare. This course can be useful for a Quantitative Analyst because it will help them build a foundation in linear algebra, which is essential for understanding data and building models.
Actuary
Actuary
An Actuary is responsible for assessing and managing risk. They use mathematical and statistical methods to calculate the probability of events and the potential financial impact of these events. This course may be useful for an Actuary because it can help them build a foundation in linear algebra, which is essential for understanding risk and building models.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. They work in a variety of industries, including healthcare, education, and government. This course can be useful for a Statistician because it will help them build a foundation in linear algebra, which is essential for understanding data and building models.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical methods to improve the efficiency and effectiveness of operations. They work in a variety of industries, including manufacturing, transportation, and healthcare. This course can be useful for an Operations Research Analyst because it will help them build a foundation in linear algebra, which is essential for understanding data and building models.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making recommendations. They work in a variety of industries, including investment banking, commercial banking, and asset management. This course may be useful for a Financial Analyst because it can help them build a foundation in linear algebra, which is essential for understanding financial data and building models.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business data and making recommendations. They work in a variety of industries, including consulting, technology, and healthcare. This course may be useful for a Business Analyst because it can help them build a foundation in linear algebra, which is essential for understanding data and building models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work in a variety of industries, including technology, finance, and healthcare. This course can be useful for a Software Engineer because it will help them build a foundation in linear algebra, which is essential for understanding data structures and algorithms.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines. They work in a variety of industries, including technology, finance, and healthcare. This course can be useful for a Data Engineer because it will help them build a foundation in linear algebra, which is essential for understanding data structures and algorithms.
Computer Scientist
Computer Scientist
A Computer Scientist designs, develops, and analyzes computer systems. They work in a variety of industries, including technology, finance, and healthcare. This course can be useful for a Computer Scientist because it will help them build a foundation in linear algebra, which is essential for understanding data structures and algorithms.
Mathematician
Mathematician
A Mathematician develops new mathematical theories and solves mathematical problems. They work in a variety of industries, including academia, research, and industry. This course can be useful for a Mathematician because it will help them build a foundation in linear algebra, which is essential for understanding mathematics.
Physicist
Physicist
A Physicist studies the fundamental laws of the universe. They work in a variety of industries, including academia, research, and industry. This course may be useful for a Physicist because it can help them build a foundation in linear algebra, which is essential for understanding physics.
Engineer
Engineer
An Engineer designs, builds, and maintains machines, structures, and systems. They work in a variety of industries, including manufacturing, construction, and transportation. This course may be useful for an Engineer because it can help them build a foundation in linear algebra, which is essential for understanding engineering.
For more career information including salaries, visit:
OpenCourser.com/course/fzq7p9/essential
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
Essential Linear Algebra for Data Science.
Provides a comprehensive introduction to linear algebra, covering the basics as well as more advanced topics. It valuable reference for students and professionals in data science, as it provides a strong foundation in the mathematical concepts underlying many data science techniques.
Concise and accessible introduction to linear algebra, with a focus on applications in data science and machine learning. It covers the essential concepts of linear algebra, such as matrices, vectors, and eigenvalues, and shows how they can be used to solve real-world problems.
Provides a comprehensive treatment of matrix analysis and applied linear algebra, with a focus on applications in data science and engineering. It covers a wide range of topics, including matrix theory, linear equations, and eigenvalue problems.
Rigorous and comprehensive introduction to linear algebra, with a focus on theoretical concepts. It valuable resource for students and professionals who want to develop a deep understanding of the subject.
Classic textbook on linear algebra, with a focus on both theoretical and applied aspects. It valuable resource for students and professionals who want to develop a strong foundation in the subject.
Save
Is an introduction to abstract algebra, which is the study of algebraic structures such as groups, rings, and fields. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Is an introduction to topology, which is the study of the properties of geometric shapes. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Save
Is an introduction to real analysis, which is the study of the real numbers and their properties. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Save
Is an introduction to complex analysis, which is the study of the complex numbers and their properties. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Save
Is an introduction to measure theory, which is the study of the mathematical foundations of probability and integration. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Is an introduction to functional analysis, which is the study of vector spaces and their applications. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Is an introduction to differential geometry, which is the study of the geometry of smooth manifolds. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Classic textbook on statistical learning, which is the study of how to use data to make predictions. It valuable resource for students and professionals who want to develop a strong foundation in the statistical methods used in data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fzq7p9/essential
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Effort
5 weeks of study
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
James Bird
Language
English
Transcripts
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This course belongs to a
collection
called
Expressway to Data Science: Essential Math. It's comprised of three courses:
- Integral Calculus and Numerical Analysis for Data Science
- Algebra and Differential Calculus for Data Science
- Essential Linear Algebra for Data Science (viewing)
Good to know
Targets students lacking a math background, making it accessible to those new to the field
Emphasizes practical applications, ensuring learners can apply their knowledge in real-world scenarios
Provides a solid foundation in Linear Algebra, preparing learners for advanced data science concepts and applications
Facilitates a deeper understanding of Linear Algebra by using approachable methods and concepts
Focuses on key Linear Algebra concepts, avoiding unnecessary theoretical content
Designed to prepare learners for the Statistical Modeling for Data Science Application course in the CU Boulder's Master of Science in Data Science program
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