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Essential Linear Algebra for Data Science

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.

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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.
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Traffic lights

Read about what's good
what should give you pause
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

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Reviews summary

Practical linear algebra for data science

According to learners, this course receives largely positive feedback, proving highly effective for those with a limited math background seeking to apply linear algebra in data science. Students commend its clear and approachable explanations, emphasizing practical applications over abstract theory. It delivers excellent foundational knowledge crucial for the field. However, its deliberate focus on essential concepts means less emphasis on rigorous proofs or deep theoretical aspects, which is a minor consideration for advanced learners seeking comprehensive mathematical rigor.
Organized content with concise modules and helpful visualizations.
"The course structure flows logically, building concepts step by step without overwhelming."
"I found the bite-sized modules very effective for learning linear algebra without getting lost."
"The visual aids and examples truly helped solidify my understanding of abstract concepts."
The instructor explains difficult topics in an easy-to-understand way.
"The instructor's explanations are superb, breaking down complex ideas into digestible chunks."
"I found the lectures engaging and easy to follow, even when the concepts were new to me."
"My understanding improved significantly due to the clear and patient teaching style."
Directly applies linear algebra concepts to data science.
"It teaches exactly what you need for machine learning and data analysis, skipping the irrelevant parts."
"The examples were really practical and showed me how linear algebra is used in real-world data science problems."
"I now understand the math behind many algorithms I use daily, thanks to this course's practical focus."
Highly accessible for learners new to linear algebra.
"As someone who struggles with math, I found the explanations incredibly clear and intuitive, making complex topics manageable."
"This course is perfect if you don't have a strong math background but need the linear algebra for data science."
"I appreciate how it simplifies concepts without getting bogged down in unnecessary proofs, which helped me grasp the fundamentals."
Focuses on application over rigorous mathematical proofs.
"If you're looking for deep mathematical proofs and derivations, this might not be the course for you."
"While excellent for practical use, I sometimes wished for a bit more theoretical grounding in certain areas."
"I got what I needed for DS, but I'll have to look elsewhere for a more advanced, proof-based linear algebra course."

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
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
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
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
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
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
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
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
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.

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
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning Engineer
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.
See salaries and explore the career path for Data 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.
See salaries and explore the career path for Quantitative Analyst
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.
See salaries and explore the career path for Actuary
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Operations Research 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.
See salaries and explore the career path for Financial 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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Software 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.
See salaries and explore the career path for Data Engineer
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.
See salaries and explore the career path for Computer Scientist
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.
See salaries and explore the career path for Mathematician
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.
See salaries and explore the career path for Physicist
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.
See salaries and explore the career path for Engineer

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.
Linear Algebra and Its Applications
Save
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.
Linear Algebra and Its Applications
Paperback
Check
Linear Algebra and Its Applications
Kindle Edition
Check
Cover image
Introduction to Applied Linear Algebra
Save
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.
Introduction to Applied Linear Algebra: Vectors,...
Hardcover
$$
Introduction to Applied Linear Algebra: Vectors,...
Kindle Edition
$$
Cover image
Matrix Analysis and Applied Linear Algebra
Save
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.
Matrix Analysis and Applied Linear Algebra, Second...
Paperback
$$$
Cover image
Linear Algebra Done Right
Save
Rigorous and comprehensive introduction to linear algebra, with a focus on theoretical concepts. It valuable resource for students and professionals who want to develop a deep understanding of the subject.
Linear Algebra Done Right (Undergraduate Texts in...
Hardcover
$$
Linear Algebra Done Right (Undergraduate Texts in...
Kindle Edition
$$
Linear Algebra Done Right (Undergraduate Texts in...
Hardcover
$$
Linear Algebra Done Right (Undergraduate Texts in...
Paperback
$$$$
(Español) Aljebra lineala ondo egina
Paperback
$$
Cover image
Modern Analysis (1997)
Save
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.
Modern Analysis (1997) (CRC Press Revivals)
Kindle Edition
$$$
Cover image
Abstract Algebra
Save
Is an introduction to abstract algebra, which is the study of algebraic structures such as groups, rings, and fields. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Abstract Algebra
Hardcover
$$$$
Abstract Algebra by I. N. Herstein (1986-04-03)
Hardcover
$$$$
Abstract Algebra 3rd edition by Herstein, I. N....
Paperback
$$$$
Cover image
ELEMENTS OF ALGEBRAIC TOPOLOGY
Save
Is an introduction to topology, which is the study of the properties of geometric shapes. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Elements Of Algebraic Topology
Kindle Edition
$$$
Cover image
Real Analysis
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.
Real Analysis 4e
Paperback
$$
Real Analysis
Hardcover
$$$$
Real Analysis
Hardcover
$$$
Real Analysis by Halsey Royden (1988-02-02)
Hardcover
$$$$
Cover image
Functional Analysis
Save
Is an introduction to complex analysis, which is the study of the complex numbers and their properties. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Functional Analysis: Introduction to Further Topics...
Hardcover
$$$
Functional Analysis: Introduction to Further Topics...
Kindle Edition
$$$
Cover image
Measure Theory
Save
Is an introduction to measure theory, which is the study of the mathematical foundations of probability and integration. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Measure Theory
Paperback
Check
Measure Theory
Kindle Edition
Check
Principles of Mathematical Analysis Textbook by...
Save
Is an introduction to functional analysis, which is the study of vector spaces and their applications. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Principles of Mathematical Analysis Textbook by...
Paperback
Check
Principles of Mathematical Analysis Textbook by...
Kindle Edition
Check
Cover image
Differential Geometry of Curves and Surfaces
Save
Is an introduction to differential geometry, which is the study of the geometry of smooth manifolds. It valuable resource for students and professionals who want to develop a deeper understanding of the mathematical foundations of data science.
Differential Geometry of Curves and Surfaces:...
Paperback
$$
Differential Geometry of Curves and Surfaces:...
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
Classic textbook on statistical learning, which is the study of how to use data to make predictions. It valuable resource for students and professionals who want to develop a strong foundation in the statistical methods used in data science.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$

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Course image
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:
  1. Integral Calculus and Numerical Analysis for Data Science
  2. Algebra and Differential Calculus for Data Science
  3. Essential Linear Algebra for Data Science (viewing)
Traffic lights
Read about what's good
what should give you pause
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
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