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Overview: Data Science is an exciting, emerging research area in the forthcoming digital age. It draws from a multitude of disciplines. We intend to show the reader the fundamental role of linear algebra in Data Science. This book showcases various Data Science topics as seen through the lens of linear algebra. This textbook explores applications of linear algebra in Data Science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course. We assume the reader is either familiar with foundational results in linear algebra or willing to consult a linear algebra text of their choice for specific results as they read our text. Readers with the basic linear algebra knowledge and who are interested in Data Science courses will find our text useful. Linear algebra is a pillar for Data Science, and understanding this will enable the student to grasp the procedures and techniques used. It will also provide the student with the ability to go further into the Data Science paradigm.
Genre: Non-Fiction > Tech & Devices
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