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⇱ Introduction to Linear Algebra and Python | Coursera


Introduction to Linear Algebra and Python

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Introduction to Linear Algebra and Python

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Gain insight into a topic and learn the fundamentals.
4.4

16 reviews

Beginner level
No prior experience required
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
4.4

16 reviews

Beginner level
No prior experience required
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Build your subject-matter expertise

This course is part of the Linear Algebra for Data Science Using Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This course is the first of a series that is designed for beginners who want to learn how to apply basic data science concepts to real-world problems. You might be a student who is considering pursuing a career in data science and wanting to learn more, or you might be a business professional who wants to apply some data science principles to your work. Or, you might simply be a curious, lifelong learner intrigued by the powerful tools that data science and math provides. Regardless of your motivation, we’ll provide you with the support and information you need to get started.

In this course, we'll cover the fundamentals of linear algebra, including systems of linear equations, matrix operations, and vector equations. Whether you’ve learned some of these concepts before and are looking for a refresher or you’re brand new to the ideas we’ll cover, you’ll find the materials to support you. Let's get started!

In module 1, you'll learn how to explain fundamental concepts of linear algebra and how to use Python, one of the most powerful programming languages, to model different data. We will cover the following learning objectives.

What's included

15 videos6 readings3 assignments2 discussion prompts

15 videosβ€’Total 85 minutes
  • Introduction to Linear Algebra for Data Science Using Python (Specialization)β€’4 minutes
  • Introduction to Linear Algebra and Pythonβ€’3 minutes
  • Introduction to Instructorsβ€’4 minutes
  • Installing the Version Control System Git Bashβ€’6 minutes
  • Installing Git Bash for a Macβ€’4 minutes
  • Installing Jupyter Notebook via Anacondaβ€’5 minutes
  • Opening a Jupyter Notebookβ€’8 minutes
  • How to Document Your Codeβ€’7 minutes
  • Introduction to Matricesβ€’5 minutes
  • Introduction to Matrices in Python Using NumPyβ€’7 minutes
  • Introduction to Matrices in Python Using SymPyβ€’6 minutes
  • How Is Linear Algebra Used to Solve Problems?β€’4 minutes
  • Using Graphs in Python to Model Dataβ€’9 minutes
  • Doing More with Graphs in Python to Model Data: Part 1β€’8 minutes
  • Doing More with Graphs in Python to Model Data: Part 2 (Data Visualizations)β€’5 minutes
6 readingsβ€’Total 86 minutes
  • Course 1 Welcomeβ€’30 minutes
  • Note About Git Bash for Macβ€’1 minute
  • Reminder - Start with Git Bashβ€’5 minutes
  • Best Practices for Learning Programmingβ€’10 minutes
  • Python Resourcesβ€’30 minutes
  • Get to Know NumPy and SymPyβ€’10 minutes
3 assignmentsβ€’Total 90 minutes
  • Fundamental Concepts in Linear Algebra & Common Data Science Applications of Linear Algebraβ€’30 minutes
  • Getting Started with Linear Algebra and Pythonβ€’30 minutes
  • Modeling Data in Pythonβ€’30 minutes
2 discussion promptsβ€’Total 20 minutes
  • Meet and Greetβ€’10 minutes
  • Practical Uses of Linear Algebraβ€’10 minutes

Let's recap! In module 1, you performed software installation, learned some best practices, and learned how graphs are used to model data in Python. In module 2, you'll gain the knowledge you need to use linear algebra to solve data science problems. You'll also perform matrix algebra on large data sets using Python. We will cover the following learning objectives.

What's included

7 videos1 reading3 assignments1 discussion prompt

7 videosβ€’Total 46 minutes
  • Introduction to Linear Algebra Functions in Pythonβ€’6 minutes
  • Using Matrices in Pythonβ€’6 minutes
  • Solving Linear Equations Using Pythonβ€’7 minutes
  • Matrix Additionβ€’6 minutes
  • Matrix Multiplicationβ€’8 minutes
  • A Practical Example of Matrix Multiplicationβ€’3 minutes
  • Using Matrix Algebra in Pythonβ€’10 minutes
1 readingβ€’Total 30 minutes
  • Supplemental Reading on Using Python to Solve Linear Equationsβ€’30 minutes
3 assignmentsβ€’Total 90 minutes
  • Matrix Addition, Multiplication, and Scalar Multiplication by Hand and in Pythonβ€’30 minutes
  • Using Linear Algebraβ€’30 minutes
  • Matrix Algebra by Hand and in Pythonβ€’30 minutes
1 discussion promptβ€’Total 10 minutes
  • Matrices at Workβ€’10 minutes

Let's recap! In module 2, you learned how to use linear algebra to solve data science problems. Using Python, you also learned how to perform matrix algebra on large data sets. In module 3, you will learn how to define vector equations and use vector equations to model data. We will cover the following learning objectives.

What's included

7 videos3 assignments1 discussion prompt

7 videosβ€’Total 42 minutes
  • Systems of Linear Equationsβ€’5 minutes
  • Row Echelon Form and Augmented Matricesβ€’8 minutes
  • Elementary Row Operations and Row Equivalent Matricesβ€’5 minutes
  • Gaussian Elimination (row reduction)β€’8 minutes
  • Vector Equationsβ€’5 minutes
  • Solving Vector Equationsβ€’8 minutes
  • Practical Applications of a Linear Function Modelβ€’4 minutes
3 assignmentsβ€’Total 90 minutes
  • Vector Equations, Systems of Linear Equations, and Modeling Dataβ€’30 minutes
  • Vector Equations and Systems of Linear Equationsβ€’30 minutes
  • Using Vector Equationsβ€’30 minutes
1 discussion promptβ€’Total 10 minutes
  • Vector Equationsβ€’10 minutes

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:- Version control - Git Bash, Jupyter Notebook via Anaconda, NumPy and SymPy, and other software tools, Modeling data, Matrix algebra and, Vector equations. In the final module of the course, you'll apply what you've learned to concrete, real-world examples. You'll practice using vector equations to study data sets and provide peer reviews. We will cover the following learning objectives.

What's included

4 videos1 reading1 assignment1 peer review

4 videosβ€’Total 19 minutes
  • Introduction to a Sample Data Setβ€’5 minutes
  • Working through a Sample Data Set Using Vector Equations: Part 1β€’5 minutes
  • Working through a Sample Data Set Using Vector Equations: Part 2β€’4 minutes
  • Real-World Applications - Ice Cream Salesβ€’5 minutes
1 readingβ€’Total 30 minutes
  • Applying Peer Feedbackβ€’30 minutes
1 assignmentβ€’Total 30 minutes
  • Real-World Data Sets and Vector Equationsβ€’30 minutes
1 peer reviewβ€’Total 45 minutes
  • Real-World Applications Assignment Instructionsβ€’45 minutes

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Instructors

Instructor ratings
3.2 (5 ratings)
Howard University
4 Coursesβ€’6,345 learners

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