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Dennis Davenport
and
MOUSSA DOUMBIA
In this course, you'll be introduced to finding inverses and matrix algebra using Python. You will also practice using row reduction to solve linear equations as well as practice how to define linear transformations. Let's get started!
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What's inside
Syllabus
Introduction to Finding Inverses
In module 1, you’ll learn how to define linear equations, how to use Python to find the determinant of matrices and how to perform different commands using Python. We will cover the following learning objectives.
Read more
Syllabus
Introduction to Finding Inverses
In module 1, you’ll learn how to define linear equations, how to use Python to find the determinant of matrices and how to perform different commands using Python. We will cover the following learning objectives.
Read more
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Builds a solid mathematical foundation in linear algebra, an essential area of knowledge in engineering, computer science, and data science
Develops skills in manipulating, representing, and solving linear equations and matrices, which are widely applicable in various domains
Introduces the concept of linear transformations and their applications in real-world scenarios, enhancing the understanding of vector spaces
Teaches the computation of determinants and inverses of matrices, which are essential concepts for solving systems of linear equations
Provides hands-on practice in applying matrix algebra and linear transformations using Python, which enhances the practical understanding of the concepts
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Reviews summary
Foundational linear algebra with python application
According to students, this course provides a strong foundation in linear algebra concepts, particularly highlighting its effective integration with Python for practical applications. Learners frequently praise the clear explanations and the instructor's engaging teaching style, which makes complex mathematical topics accessible. While many found it an excellent introduction, some more experienced learners noted it might be too basic if one already possesses a solid understanding of the subject, suggesting a focus on fundamental rather than advanced topics. The hands-on coding exercises are a consistent positive, enabling immediate application of learned theory.
Sufficient material to learn the concepts without external resources.
"I found all the necessary information within the course lectures and practice problems."
"Unlike other courses, I didn't feel the need to constantly consult outside resources to grasp the material."
"It's a comprehensive package for someone starting with linear algebra and Python."
The course follows a logical and easy-to-follow structure.
"The modules flowed seamlessly from one topic to the next, which made learning very smooth."
"I liked how new concepts were built upon previous ones, making the learning path clear."
"The pace felt just right for a foundational course, not too rushed but always moving forward."
The instructor's clear explanations made complex topics accessible.
"The instructor made even the trickiest parts of linear algebra understandable with their patient explanations."
"I really enjoyed the teaching style; it was engaging and kept me motivated throughout the modules."
"Their ability to break down complex ideas into manageable pieces was a huge plus for me."
Provides a clear and robust introduction for beginners.
"As someone new to linear algebra, I found the explanations clear and the progression logical."
"The fundamental concepts were explained exceptionally well, building a solid base for further study."
"I now have a much better grasp of determinants, matrices, and linear equations thanks to this course."
Excellent for applying concepts with real-world Python examples.
"The hands-on Python exercises really helped solidify my understanding of the theoretical concepts."
"I appreciated how every linear algebra concept was immediately followed by a practical Python implementation."
"This course taught me how to actually *use* linear algebra in my data science projects, which was invaluable."
May be too basic for those with prior experience.
"For someone who already knows some linear algebra, this course felt a bit too introductory."
"I was hoping for more advanced topics or optimization techniques, but it stayed quite fundamental."
"While good for beginners, I found myself skipping sections as I already knew the material."
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 Fundamental Linear Algebra Concepts with Python with these
activities:
Review Python basics
Show steps
Familiarize yourself with the basics of Python to ensure a stronger conceptual foundation and smoother sailing through the course.
Browse courses on
Python
Show steps
-
Revisit tutorials on Python syntax and data types
-
Solve simple coding problems using Python
-
Review documentation for commonly used Python libraries
Participate in study groups
Show steps
Engage in peer-led study sessions to discuss concepts, solve problems collaboratively, and reinforce your understanding of the course material.
Show steps
-
Form or join a study group with peers
-
Establish a regular meeting schedule
-
Take turns presenting concepts and leading discussions
Explore Python libraries for matrix operations
Show steps
Gain familiarity with Python libraries that streamline matrix operations, complementing your understanding of matrix algebra techniques.
Browse courses on
Python Libraries
Show steps
-
Identify suitable Python libraries for matrix manipulation
-
Follow tutorials on implementing matrix operations using these libraries
-
Experiment with the libraries to solve practical problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve linear equation exercises
Show steps
Practice solving linear equations to enhance your understanding of the concepts and techniques covered in the course.
Show steps
-
Attempt practice problems on finding inverses
-
Solve linear equation systems using row reduction
-
Apply your knowledge to real-world scenarios
Develop a cheat sheet for linear transformations
Show steps
Create a concise cheat sheet summarizing key concepts and formulas related to linear transformations, aiding in quick reference and knowledge retention.
Browse courses on
Linear Transformations
Show steps
-
Gather relevant information on linear transformations
-
Organize and present the information in a clear and concise manner
-
Review and refine the cheat sheet for effectiveness
Participate in online coding challenges
Show steps
Test and refine your Python skills and problem-solving abilities by participating in coding challenges related to linear algebra and matrix operations.
Browse courses on
Coding Challenges
Show steps
-
Identify online platforms that host coding challenges
-
Select challenges that align with the course topics
-
Attempt to solve the challenges within the specified time limit
Assist fellow students as a tutor
Show steps
Sharpen your understanding of the course concepts by explaining them to others, reinforcing your knowledge and fostering a collaborative learning environment.
Show steps
-
Identify opportunities to provide tutoring or assistance
-
Prepare and plan your tutoring sessions
-
Provide clear and patient explanations to your tutees
Review Python basics
Show steps
Familiarize yourself with the basics of Python to ensure a stronger conceptual foundation and smoother sailing through the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit tutorials on Python syntax and data types
- Solve simple coding problems using Python
- Review documentation for commonly used Python libraries
Participate in study groups
Show steps
Engage in peer-led study sessions to discuss concepts, solve problems collaboratively, and reinforce your understanding of the course material.
Show steps
Show steps
Show steps
- Form or join a study group with peers
- Establish a regular meeting schedule
- Take turns presenting concepts and leading discussions
Explore Python libraries for matrix operations
Show steps
Gain familiarity with Python libraries that streamline matrix operations, complementing your understanding of matrix algebra techniques.
Browse courses on
Python Libraries
Show steps
Show steps
Show steps
- Identify suitable Python libraries for matrix manipulation
- Follow tutorials on implementing matrix operations using these libraries
- Experiment with the libraries to solve practical problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve linear equation exercises
Show steps
Practice solving linear equations to enhance your understanding of the concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Attempt practice problems on finding inverses
- Solve linear equation systems using row reduction
- Apply your knowledge to real-world scenarios
Develop a cheat sheet for linear transformations
Show steps
Create a concise cheat sheet summarizing key concepts and formulas related to linear transformations, aiding in quick reference and knowledge retention.
Browse courses on
Linear Transformations
Show steps
Show steps
Show steps
- Gather relevant information on linear transformations
- Organize and present the information in a clear and concise manner
- Review and refine the cheat sheet for effectiveness
Participate in online coding challenges
Show steps
Test and refine your Python skills and problem-solving abilities by participating in coding challenges related to linear algebra and matrix operations.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Identify online platforms that host coding challenges
- Select challenges that align with the course topics
- Attempt to solve the challenges within the specified time limit
Assist fellow students as a tutor
Show steps
Sharpen your understanding of the course concepts by explaining them to others, reinforcing your knowledge and fostering a collaborative learning environment.
Show steps
Show steps
Show steps
- Identify opportunities to provide tutoring or assistance
- Prepare and plan your tutoring sessions
- Provide clear and patient explanations to your tutees
Career center
Learners who complete Fundamental Linear Algebra Concepts with Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. This course may be useful for a Machine Learning Researcher because it provides a foundation in linear algebra, which is used in many machine learning algorithms.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make better decisions. This course may be useful for a Data Scientist because it provides a foundation in linear algebra, which is used in many data science techniques.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use machine learning algorithms to solve problems and make predictions. This course may be useful for a Machine Learning Engineer because it provides a foundation in linear algebra, which is used in many machine learning algorithms.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers use mathematical and statistical models to make investment decisions. This course may be useful for a Quantitative Researcher because it provides a foundation in linear algebra, which is used in many quantitative finance models.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make better decisions. This course may be useful for a Data Analyst because it provides a foundation in linear algebra, which is used in many data analysis techniques.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. This course may be useful for a Statistician because it provides a foundation in linear algebra, which is used in many statistical techniques.
Financial Engineer
Financial Engineer
Financial Engineers use mathematical and financial models to develop new financial products and strategies. This course may be useful for a Financial Engineer because it provides a foundation in linear algebra, which is used in many financial engineering models.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course may be useful for a Financial Analyst because it provides a foundation in linear algebra, which is used in many financial models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency of organizations. This course may be useful for an Operations Research Analyst because it provides a foundation in linear algebra, which is used in many operations research models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to make investment decisions. This course may be useful for a Quantitative Analyst because it provides a foundation in linear algebra, which is used in many quantitative finance models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for a Software Engineer because it provides a foundation in linear algebra, which is used in many software applications.
Risk Analyst
Risk Analyst
Risk Analysts use mathematical and statistical techniques to assess risk and make informed decisions. This course may be useful for a Risk Analyst because it provides a foundation in linear algebra, which is used in many risk assessment models.
Researcher
Researcher
Researchers use scientific methods to conduct research and discover new knowledge. This course may be useful for a Researcher because it provides a foundation in linear algebra, which is used in many research fields.
Actuary
Actuary
Actuaries use mathematical and statistical skills to assess risk and make informed decisions. This course may be useful for an Actuary because it provides a foundation in linear algebra and matrix algebra, both of which are heavily used in the field of actuarial science.
Teacher
Teacher
Teachers educate students at all levels. This course may be useful for a Teacher because it provides a foundation in linear algebra, which is taught in many math and science courses.
For more career information including salaries, visit:
OpenCourser.com/course/lwwaky/fundamental
Reading list
We've selected 11 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
Fundamental Linear Algebra Concepts with Python.
Comprehensive treatment of linear algebra with applications. It covers a wide range of topics, including matrices, vectors, determinants, eigenvalues, and eigenvectors.
Save
Comprehensive treatment of linear algebra. It covers a wide range of topics, including matrices, vectors, determinants, eigenvalues, and eigenvectors.
Comprehensive treatment of linear algebra. It covers a wide range of topics, including matrices, vectors, determinants, eigenvalues, and eigenvectors.
Classic textbook on linear algebra that provides a comprehensive overview of the subject. It is well-written and easy to follow, making it a great resource for both students and professionals. The book covers a wide range of topics, including matrices, vectors, determinants, eigenvalues, and eigenvectors.
Comprehensive treatment of matrix analysis and applied linear algebra. It covers a wide range of topics, including matrix norms, singular value decomposition, and applications to statistics and machine learning.
Comprehensive treatment of linear algebra with applications. It covers a wide range of topics, including matrices, vectors, determinants, eigenvalues, and eigenvectors.
Comprehensive treatment of applied linear algebra. It covers a wide range of topics, including matrix decompositions, iterative methods for solving linear systems, and eigenvalue problems.
Comprehensive treatment of linear algebra with applications. It covers a wide range of topics, including matrices, vectors, determinants, eigenvalues, and eigenvectors.
More concise introduction to linear algebra than Strang's book. It is also well-written and easy to follow, but it covers a narrower range of topics. However, it does include some topics that are not covered in Strang's book, such as inner product spaces and orthogonal projections.
Comprehensive treatment of numerical linear algebra. It covers a wide range of topics, including matrix decompositions, iterative methods for solving linear systems, and eigenvalue problems.
More advanced treatment of linear algebra than the previous two books. It is not as easy to follow, but it covers a wider range of topics, including abstract vector spaces, linear transformations, and multilinear algebra.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lwwaky/fundamental
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Rating
4.5
Effort
10 hours to complete
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 (viewing)
- Introduction to Linear Algebra and Python
- Capstone: Data Science Problem in Linear Algebra Framework
- Building Regression Models with Linear Algebra
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