From Concepts to Code: Introduction to Data ScienceКНИГИ » ПРОГРАММИНГ
Название: From Concepts to Code: Introduction to Data Science Автор: Adam P. Tashman Издательство: CRC Press Год: 2024 Страниц: 386 Язык: английский Формат: pdf (true) Размер: 17.4 MB
The breadth of problems that can be solved with Data Science is astonishing, and this book provides the required tools and skills fot a broad audience. The reader takes a journey into the forms, uses, and abuses of data and models, and learns how to critically examine each step. Python coding and data analysis skills are built from the ground up, with no prior coding experience assumed. The necessary background in Computer Science, mathematics, and statistics is provided in an approachable manner.
This text uses Python because it is one of the most popular languages for Data Science, it supports the tasks we need to accomplish, and it is relatively easy to learn. It will teach Python by diving into problems with data and solving challenges. It will not methodically catalog what each operator does, what each control structure looks like, and so forth. There are excellent references for learning in this way, with suggestions given later. Finally, the book includes many exercises across different topics, with the hope that they are interesting and motivating.
Each step of the Machine Learning lifecycle is discussed, from business objective planning to monitoring a model in production. This end-to-end approach supplies the broad view necessary to sidestep many of the pitfalls that can sink a Data Science project. Detailed examples are provided from a wide range of applications and fields, from fraud detection in banking to breast cancer classification in healthcare. The reader will learn the techniques to accomplish tasks that include predicting outcomes, explaining observations, and detecting patterns. Improper use of data and models can introduce unwanted effects and dangers to society. A chapter on model risk provides a framework for comprehensively challenging a model and mitigating weaknesses. When data is collected, stored, and used, it may misrepresent reality and introduce bias. Strategies for addressing bias are discussed. From Concepts to Code: Introduction to Data Science leverages content developed by the author for a full-year Data Science course suitable for advanced high school or early undergraduate students.
This book is a first course in Data Science. It will teach essential skills including: • How to use Git and GitHub to work with a code repository • Coding in Python to process, summarize, transform, and visualize data • Classical Machine Learning tasks: regression, classification, and clustering using Sklearn • Troubleshooting errors, which are a daily part of the life of a data scientist
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From Concepts to Code: Introduction to Data Science 
