Preface
Linear Algebra serves as a foundational tool for advanced analytical methods in finance, business, and machine learning. This book aims to connect theoretical concepts with practical applications, helping readers harness the power of Linear Algebra in real-world scenarios.
Each chapter introduces key topics such as vectors, matrices, systems of linear equations, linear transformations, eigenvalues, and Singular Value Decomposition (SVD). Real-world examples and case studies demonstrate how these concepts can solve complex problems and drive strategic decision-making. Practical exercises at the end of each chapter encourage hands-on learning, and guidance on using R and Python will enable readers to implement these techniques effectively.
I extend my gratitude to everyone who supported this project, especially my family, friends, colleagues, and students. I hope this book serves as a valuable resource for students and professionals alike, inspiring you to explore the impact of Linear Algebra in today’s data-driven world.
Advantages of This Book
This book offers a practical and applied approach, where the discussion not only focuses on the theory of Linear Algebra but also on its application in the real world, especially in the fields of finance, business, and machine learning. Each concept is accompanied by relevant case studies, such as portfolio optimization, risk management, and data analysis. The material is organized systematically, from basic to advanced topics, allowing readers to understand each section thoroughly before moving on to more complex concepts. This is particularly beneficial for those new to Linear Algebra or those wishing to deepen their understanding.
Additionally, this book emphasizes the applications of Linear Algebra in the digital age, especially in data science and machine learning, which have become essential in various industries. Readers will also be introduced to supporting software such as R and Python, which are used for simulations, calculations in Linear Algebra, and data analysis, providing practical skills that are highly valuable in the workforce.
Another advantage of this book is the inclusion of case studies and real examples in each chapter, which help readers directly see how Linear Algebra concepts can be applied to solve real problems. Examples include regression analysis in finance and image and text processing in machine learning. This book is also suitable for a wide range of readers, from students to professionals, with explanations that are easy to understand, clear illustrations, and examples that effectively clarify abstract concepts.
This book strikes a balance between theory and practice, allowing readers to grasp Linear Algebra concepts while also learning how to apply them in real-world scenarios. Furthermore, it employs an interdisciplinary approach, linking Linear Algebra with fields such as economics, business, and technology, thus providing broader insights into the contributions of Linear Algebra across various industrial sectors.
About the Author
Bakti serves as a lecturer in the Data Science Program at ITSB. He earned his Master’s degree from the Department of Applied Mathematics at National Sun Yat Sen University, Taiwan. In addition to teaching, Bakti also works as a freelance Data Scientist for leading companies such as JNE, Samora Group, Pertamina, and PT. Green City Traffic. He has a special enthusiasm for working on projects (teaching) in the fields of Big Data Analytics, Machine Learning, Optimization, and Time Series Analysis in finance and investment. His main expertise lies in statistical programming languages such as R Studio and Python. He is also experienced in implementing database systems such as MySQL/NoSQL for data management and proficient in using Big Data tools like Spark and Hadoop. Some of his projects can be found at the following links: Rpubs, GitHub, Website, and Kaggle.
Acknowledgments
With heartfelt gratitude, I would like to thank everyone who contributed to the preparation of this book. I extend special thanks to my beloved family for their support, patience, and understanding throughout the writing process. I also thank my colleagues and academic peers for their valuable feedback and constructive criticism, which have helped refine this book. I greatly appreciate the contributions of the students who provided inspiration and motivation, as well as the publishing team for their professionalism in assisting with the publication process. I hope this book can benefit readers and contribute to the advancement of knowledge in finance, business, and machine learning.
Feedback & Suggestions
Constructive suggestions regarding aspects that need to be added or clarified are greatly appreciated, as they can help improve the quality of this book. Additionally, if there are specific topics that you find relevant and would like to explore further, please feel free to share those ideas. Feedback from readers will not only enrich the learning experience but also assist the author in crafting better works in the future.
Readers/users who wish to provide feedback and suggestions are invited to do so through the contact information below:
- dsciencelabs@outlook.com
- siregarbakti@gmail.com
- siregarbakti@itsb.ac.id