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.

This course is part of the Linear Algebra for Data Science Using Python Specialization

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Linear Algebra for Data Science Using Python SpecializationHoward University

## About this Course

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Course 1 of 4 in the

Beginner Level

Approx. 12 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for BusinessFlexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Beginner Level

Approx. 12 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business## Offered by

## Syllabus - What you will learn from this course

**5 hours to complete**

### Introduction to Matrices and Linear Algebra

**5 hours to complete**

15 videos (Total 85 min), 6 readings, 3 quizzes

**3 hours to complete**

### Using Linear Algebra Concepts in Python

**3 hours to complete**

7 videos (Total 46 min), 1 reading, 3 quizzes

**2 hours to complete**

### Vector Equations and Systems of Linear Equations

**2 hours to complete**

7 videos (Total 47 min)

**2 hours to complete**

### Real-World Applications of Vector Equations

**2 hours to complete**

4 videos (Total 24 min), 1 reading, 2 quizzes

## About the Linear Algebra for Data Science Using Python Specialization

## Frequently Asked Questions

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