This Specialization is for learners interested in exploring or pursuing careers in data science or understanding some data science for their current roles. This course will build upon your previous mathematical foundations and equip you with key applied tools for using and analyzing large data sets.
Overview
Syllabus
Course 1: Introduction to Linear Algebra and Python
- Offered by Howard University. This course is the first of a series that is designed for beginners who want to learn how to apply basic data ... Enroll for free.
Course 2: Fundamental Linear Algebra Concepts with Python
- Offered by Howard University. In this course, you'll be introduced to finding inverses and matrix algebra using Python. You will also ... Enroll for free.
Course 3: Building Regression Models with Linear Algebra
- Offered by Howard University. In this course, you'll learn how to distinguish between the different types of regression models. You will ... Enroll for free.
Course 4: Capstone: Data Science Problem in Linear Algebra Framework
- Offered by Howard University. In this course, you'll review the specifics of the Capstone project. In addition, you will create and run your ... Enroll for free.
- Offered by Howard University. This course is the first of a series that is designed for beginners who want to learn how to apply basic data ... Enroll for free.
Course 2: Fundamental Linear Algebra Concepts with Python
- Offered by Howard University. In this course, you'll be introduced to finding inverses and matrix algebra using Python. You will also ... Enroll for free.
Course 3: Building Regression Models with Linear Algebra
- Offered by Howard University. In this course, you'll learn how to distinguish between the different types of regression models. You will ... Enroll for free.
Course 4: Capstone: Data Science Problem in Linear Algebra Framework
- Offered by Howard University. In this course, you'll review the specifics of the Capstone project. In addition, you will create and run your ... Enroll for free.
Courses
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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!
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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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In this course, you'll review the specifics of the Capstone project. In addition, you will create and run your regression model and share your results with your peers. Let's get started!
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In this course, you'll learn how to distinguish between the different types of regression models. You will apply the Method of Least Squares to a dataset by hand and using Python. In addition, you will learn how to employ a linear regression model to identify scenarios. Let's get started!
Taught by
Moussa Doumbia
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Reviews
1.0 rating, based on 1 Class Central review
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Much of the code in the Jupyter Notebooks has errors. Random matrices are created and those matrices are used to demonstrate finding the inverse of a matrix. Not all matrices have inverses so the code fails. This is just one example of how bad these examples are. It is pretty clear that not much thought was put into these lessons.
