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"Data science project: an inductive learning approach" provides a comprehensive methodology for data science project development, emphasizing software engineering principles essential for reliable solutions.
Prof. Dr. Filipe Verri, a senior data science project manager and professor at the Aeronautics Institute of Technology (ITA), guides readers through the origins, scope, and key concepts of data science.
This book covers machine learning, data handling, and rigorous validation techniques, all essential for preparing readers to tackle complex, real-world projects.
Contributions from Prof. Dr. Johnny Marques, also professor at ITA and an expert in critical software development, bring an industry-tested perspective to the software aspects, making this an essential guide for aspiring data scientists, researchers and seasoned professionals alike.
- A brief history of data science
- Fundamental concepts
- Data science project
- Structured data
- Data handling
- Learning from data
- Data preprocessing
- Validation
An appendix provides a brief introduction to the mathematical foundations of data science, including algorithms, set theory, linear algebra, and probability.
Prof. Dr. Filipe Verri is a senior data science project manager and professor at the Aeronautics Institute of Technology (ITA). He has a Ph.D. in computer science and computational mathematics from the University of São Paulo (USP).
This project is licensed under the Creative Commons Attribution-NonCommercial NoDerivatives 4.0 International License.
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