資料科學與分析應用來說是非常重要的知識領域，本課程將了解資料科學與工程中的主要概念和工具，概述資料分析前所需要的資料必要處理流程，包括分析問題中所關聯的資料收集、資料清理與篩選、資料儲存與索引，以及資料結構化等，並介紹相關使用工具。本課程分為兩個部分。 第一部分是對將資料(raw data)轉化為可使用的資料結構化概念性流程介紹，以及資料分析探索，其中包括資料從收集、清理、萃取、儲存載入，資料視覺化，資料探勘；第二部分則介紹程序中使用的工具實用介紹。本課程以python語言作為課程實作演練的程式語言。Data science and analysis applications are very important knowledge areas. This course will understand the main concepts and tools in data science and engineering, overview the necessary data processing processes required before data analysis, including data collection, data cleaning and screening related to the analysis of the problems, data storage and indexing, data structure, etc., and introduce relevant usage tools. This course is divided into two parts. The first part is an introduction to the conceptual process of converting raw data into usable data structures, as well as data analysis and exploration, including data collection, cleaning, extraction, storage loading, data visualization, and data exploration; the second part introduces the practical introduction of tools used in the program. This course uses python language as a programming language for course practice.
Python資料科學學習手冊
Python Data Science Handbook: Essential Tools for Working with Data
作者： Jake VanderPlas
譯者： 何敏煌
出版社：歐萊禮
Python Data Science Learning Manual
Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Translator: He Minhuang
Publisher: Ole Leather
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