This course, offered by Howard University, is designed for beginners interested in applying basic data science concepts to real-world problems. The course covers the fundamentals of linear algebra, including systems of linear equations, matrix operations, and vector equations, with a focus on using Python for practical implementation. Through a series of engaging modules, learners will gain a solid understanding of linear algebra and its applications in data science.
The comprehensive curriculum begins with an introduction to matrices and linear algebra, emphasizing their relevance to data science through Python. Subsequent modules delve into using linear algebra concepts in Python, exploring vector equations and systems of linear equations, and real-world applications of vector equations. The course is suitable for students looking to pursue a career in data science, professionals seeking to apply data science principles to their work, and curious, lifelong learners intrigued by the powerful tools that data science and math provide.
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The course modules cover a comprehensive introduction to matrices and linear algebra, followed by the application of linear algebra concepts using Python, exploring vector equations, systems of linear equations, and real-world applications of vector equations.
This module provides an introduction to matrices and linear algebra, emphasizing their relevance to data science through Python. Learners will explore the fundamentals of linear algebra and its application using Python, including installing necessary tools and understanding the practical uses of linear algebra.
In this module, learners will delve into using linear algebra concepts in Python, focusing on matrix operations and practical examples of matrix algebra. The module emphasizes the application of linear algebra functions in Python and provides supplemental reading on solving linear equations using Python.
Exploring vector equations and systems of linear equations, this module covers the fundamentals of systems of linear equations, row echelon form, Gaussian elimination, and solving vector equations. Learners will understand the practical applications of linear function modeling and vector equations in data science.
This module introduces real-world applications of vector equations, providing practical examples and assignments to apply the learned concepts to analyze and solve real-world data sets. Learners will work through sample data sets and explore the application of vector equations in scenarios such as ice cream sales.
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