近年全球各領域紛紛吹起資料科學風潮，資料科學目的是從資料中獲得洞察進而產生決策、行動及智慧應用，即是因為當今資料收集、存取與分析建模技術越趨成熟，因此不論各領域，都越來越重視資料的應用價值，並積極研發新應用。
本課程學習程式語言Python並學習資料收集、分析建模技術的資料科學，Python 這兩年來每年都被IEEE spectrum評選為全球第一名程式語言，因為它相較於其他程式語言更為簡潔、易學，且應用領域廣泛、背後社群強大，在數據分析領域，開源軟體和套件，也都是以Python作為主要實作語言。Python 強大且豐富，從資料處理、資料分析、視覺化到網頁爬蟲與遊戲、機器學習等都有相當完整的套件與函式庫可使用，包含網路爬蟲（Web Crawler）、openCV，實作深入進階的資料科學專案。
我們將讓學生從Python中學習基礎的程式邏輯與概念和python資料型態、資料結構、函式等等，資料的收集跟整理還有應用，銜接到各種資訊的預測跟機器學習。本課程為＂ 1071-1169 器學習導論與應用＂系列課程之一。
In recent years, data science has been storming in various fields around the world. The purpose of data science is to gain insight from data and generate decisions, actions and smart applications. That is, because data collection, access and analysis modeling technologies are becoming more and more mature, regardless of various fields, they are paying more and more attention to the application value of data and actively researching and developing new applications.
This course learns the programming language Python and the data science of data collection, analysis and modeling technology. Python has been selected by IEEE spectrum as the world's number one programming language every year because it is simpler and easier to learn than other programming languages, and has a wide range of applications and a strong community behind it. In the data analysis field, the source software and suites are also used by Python as the main language of practice. Python is powerful and rich. From data processing, data analysis, visualization to web crawlers, games, machine learning, etc., there are quite complete suites and functional libraries to use, including web crawlers and openCVs, to create in-depth and advanced data science projects.
We will allow students to learn basic program logic and concepts, python data types, data structures, functions, etc. from Python, and also have applications for data collection and organization, and receive prediction and machine learning of various information. This course is one of the series of courses "1071-1169 Equipment Learning Discussion and Application".
Python Data Science Handbook: Essential Tools for Working with Data
作者： Jake VanderPlas
出版社：歐萊禮
Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Publisher: Ole Leather
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 期中考期中考 Midterm exam |
20 | |
| 期末考期末考 Final exam |
30 | |
| 作業作業 Action |
40 | |
| 課堂參與(小考)課堂參與(小考) Class Participation (Small Exam) |
10 |
