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For 424 | the avoidance of doubt, this paragraph does not form part of the 425 | public licenses. 426 | 427 | Creative Commons may be contacted at creativecommons.org. 428 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 【Star Rocket】Python 學習地圖與攻略 2 | 3 | 近年爆紅的程式語言之一 Python 程式語言，每個人對它的認識與憧憬，以及學習或開發目標皆不盡相同，坊間無論是線上線下都出現了許多學習資源，往往造成真正想學習的人迷失了方向，更是有許多半路出家的人多走了許多冤妄路，浪費了許多時間，我們都明白，因此，我們希望透過更直白的方式，讓大家一覽 Python 程式語言的學習藍圖及歷程。 4 | 5 | 為甚麼要學 Python？自 1994年正式發表至今超過 20多年，已經遍佈各重要科技趨勢當中，如：數據分析、IOT 軟硬整合、人工智慧、機器人開發等重要領域，事個不可或缺的重要語言，如今更是開發出多種資料庫及套件廣泛應用在雲端服務平台上；現今使用 Python 為後端重要程式語言的新創企業如 Pinterest、Uber、instagram、Youtube 等（[參考文章](https://www.jianshu.com/p/e3df3b024b6c)），Python 語言近年更是興起成為全球最高薪的程式語言之一（文章：[中文](http://technews.tw/2017/12/21/2018-5-high-paid-in-demand-programming-language/) / [英文](https://www.techrepublic.com/article/five-highly-paid-and-in-demand-programming-languages-to-learn-in-2018/)），透過絕佳的跨平台支援性，在不同尺寸、系統設備上，操作同樣的語言環境，更是普遍使用在大數據與資料分析的領域，在全球軟硬體開發領域具有舉足輕重的地位。 6 | 7 | Python 程式語言的特色： 8 | 9 | 1. 數據分析領域通用語言：簡單且強大。 10 | 2. 語意認識易讀和易撰寫：直白且清楚。 11 | 3. 各類領域應用功能強大：應用且多元。 12 | 4. 跨平台應用且容易擴充：易整合學習。 13 | 14 | 本次課程活動：[連結](https://www.accupass.com/event/1812110559434772340930) 15 | 16 | ## 本次課程大綱： 17 | 18 | * Python 可以作甚麼？ 19 | * Python 學習地圖是甚麼？ 20 | * Python 各階段學習目標與時間性？ 21 | 22 | ## 課程學習摘要： 23 | 24 | 以下內容為經由課程隨堂筆記和上課簡報的重點彙整。 25 | 26 | 1. 程式開發常見工作職位：(和 Python 無關，軟體開發領域慨況) 27 | 1. Front-End Developer（前端開發） 28 | 2. Backend Developer（後端開發） 29 | 1. Blockchain 30 | 2. IOT 31 | 3. App Developer（App 開發） 32 | 1. Android 33 | 2. iOS 34 | 4. Data Analyst（資料分析） 35 | 5. A.I. Engineer（人工智慧工程） 36 | 2. 各領域專業分工解析：會特別解析跟 Python 有關部分。 37 | 1. Front-End Developer（前端開發）：並無與 Python 直接相關。 38 | 1. 視覺與網頁切版：HTML、CSS 39 | 2. 互動式網頁：JavaScript 40 | 2. Backend Developer（後端開發）：與 Python 有關，談到後端亂成一團，學 Python 有機會幫公司架設出一個網站，例如：電商網站。具有 Python 初階程度能有基本工作條件，但需要看很多面向，首先會需要考慮你的「程式語言」，Python 是其中一個選擇，選完會有需要學習對應的「框架」，接者是選擇「資料庫」，再來選擇「雲端服務」，如果選擇後端開發就要學習雲端服務。後端工程師需要把這整串學起來，最後才是選擇「延伸性領域」。 41 | 1. 程式語言：Python、PHP、Java 42 | 2. 框架：Flask、Django、Laravel 43 | 3. 資料庫：PostgreSQL、MySQL、MongoDB 44 | 4. 雲端服務：AWS、GCP、Azure 45 | 5. 延伸領域：Blockchain、IOT 46 | 3. App Developer（產品開發）：並無與 Python 直接相關。 47 | 1. Android：Java、Kotlin 48 | 2. iOS：Swift、Object-C 49 | 4. Data Analyst（資料分析）：早期學習資料分析，數學能力非常吃重，如果想要長期發展，遲早都需要具備數學能力，大多數據分析工具都跟 Python 有關，Python 是一個很老的程式語言，但因為近年大數據，使資料分析成為很重點的部分。 50 | 1. Data Collection: 51 | 1. Crawler - BeautifulSoup 52 | 2. Database - SQLite, MySQL 53 | 2. Data Analysis: 54 | 1. Pandas 55 | 3. Data Visualization: 56 | 1. Matplotlib 57 | 2. Tkinter 58 | 5. A.I. Engineer（進入人工智慧開發）：第一個就是深度學習，人工智慧發展蠻久了，但直到 1998 年，那年發生 IBM 電腦在西洋棋界擊敗世界冠軍，當時是人工智慧的高峰，接著是低谷，研究上幾乎沒有突破，以前當說要走 Ai 領域的人都會被笑，因為根本沒有發展，直到2016年 [AlphaGo](https://zh.wikipedia.org/wiki/AlphaGo)，在圍棋領域把人類幹掉；AI 這件事情要做的好，需要很多基礎訓練，職位也不多，公司要找工程師都要找資深，薪水十萬以上的價碼都有可能，學習歷程相對較長，要有心理準備，但在 AI 領域 Python 是大家較長使用的語言。 59 | 1. Math 60 | 1. Linear Algebra 61 | 2. Statistics 62 | 2. A.I. Fundamental 63 | 1. Artificial Neural Network 64 | 2. Convolutional Neural Network 65 | 3. Python based A.I. Library 66 | 1. TensorFlow 67 | 2. Keras 68 | 3. Python 學習方式建議： 69 | 1. 基礎語法與套件使用：*實體課程、線上課程 70 | 2. 專案練習：*跟著老師完成微小專案 71 | 3. 獨立完成可用的專案：*最關鍵點是沒人幫得了你，老師也幫不了，重點在於你的解決能力，你必須練習解決問題的能力。這是很難去訓練學生，前半年我感受最深的事情，每周花費40-70小時，一到兩個月完成。學生在這種環境中是能練得起來，有很好的環境跟同儕，去年我去Appworks School，學生一個星期要花70小時，真的是這樣，我進去半年真的是這樣，從早上寫到晚上，寫不完週末繼續，這是一個過程，這樣的訓練很難去實現它在於我收費，如果我收了錢就很難堅持我的立場，但這樣的操作模式資金的投入很大，公司不在乎賠錢，但實際是有效果的！各位如果有機會這個點上有人推你前進，但在商業上很難像Appworks School 不收費這樣做，覺悟很重要，你要花很多時間投入進去。 72 | 4. 當你卡死的時候，是多數人遇到的狀況，一開始好像會了，真正的難的是後面遇到困難很容易放棄。 73 | 4. 如何成為一名 Python 軟體工程師： 74 | 1. 程式語言：入門第一站。 75 | 1. 熟稔基本語法和運作邏輯：能正確追蹤程式。 76 | 2. 能解決定義明確的問題： 例如，給一個數字，判斷是否為質數；從檔案中讀取商品資料，並計算平均售價。 77 | 3. 能寫出乾淨的程式碼（請參考底下：本次課程資源） 78 | 2. 軟體架構：此階段重視模組化、重構程式的能力。 79 | 1. 能應用第三方套件或框架：BeautifulSoup、Pandas、TensorFlow。 80 | 2. 深入理解第三方套件或框架：類神經網路。 81 | 3. 能開發客製化的套件或框架。 82 | 3. 電腦科學：軟體工程師共同的核心能力 83 | 1. 數學：離散數學、線性代數、統計 84 | 2. 系統架構：作業系統、計算機組織、網路概論 85 | 3. 資料結構與演算法。 86 | 4. 完整發展視野：（請參考底下：本次課程資源 -> 完整簡報） 87 | 5. 當初階工程師面試時需要在意的事： 88 | * 技術能力：*履歷與作品集或經歷，技術是需要被證明，薪資也是從上面有定論。 89 | * 誠實與自覺：*聊天的形式，面試完立刻打分數，分三種：錄取、可以走了、放中間之後回來看。誠心誠意很重要，不希望裝出來。 90 | * 溝通能力與適性：*面試官在找未來的同事。 91 | 6. 現場問與答 92 | 1. Ｑ：資料分析上使用 R 跟 Python 的差異： 93 | Ａ：R 只用在資料分析上，Python 有很多應用。 94 | 2. Ｑ：Python 跟 SQL 有啥關係？ 95 | Ａ：兩者都是一個獨立的東西，程式語言挑一種搭配 SQL，Python 是把 SQL 命令下給資料庫。 96 | 3. Ｑ：什麼是資料分析？ 97 | Ａ：你如果學了 Python，就可以撈取資料與分析，甚至可以做成圖表。把關鍵字都學到一個程度，可以把整套做出來。 98 | 4. Ｑ：坊間有相關 Python 金融預測分析預測實作類似課程，我是不是學這個就夠了，因為我之後也並不會做這相關我只是需要知道此功能。 99 | Ａ：如果就只需要使用在某領域而已，是可以的，但是要更多發展學習 Python 是不夠的。 100 | 5. Ｑ：Python近幾年很紅是為什麼？ 101 | Ａ：最近因為大數據領域的崛起，導致 Python 開始紅起來，因為都跟 Python 有關。 102 | 6. Ｑ：我不是技術背景，感覺自己要寫一個網站是非常困難，當想要解決問題去用 Excel，光學 Python 是可以解決這個耗時耗工的方式？ 103 | Ａ：團隊的員工會寫一點程式，手上其實有很多Excel資料，用 Python 就可以分析，用程式自動化展現出來就是個有用的事情，但可能不會是像學習 Excel 這麼容易就是了。 104 | 105 | ## 講師介紹 106 | 107 | 彭兆蔚 - 彭彭：台大心理系、台大資工所畢業、美商昇陽 (Sun Microsystems) 校園大使、資策會軟體工程師、Tomofun 網站工程師、網頁多人線上遊戲開發、Piconion 影像處理軟體開發、類神經網路、人工智慧教學展示、COSCUP 2010、JSDC 2017 講師、iThome Modern Web 2016 - 2018 講師、世新大學、十年創業經驗；[講師相關學習資源](https://training.pada-x.com/)。 108 | 109 | ## 本次課程資源 110 | 111 | * 完整簡報：[連結](http://bit.ly/2SO7oim) 112 | * 閱讀書籍：[如何寫出乾淨的程式碼 Clean Code](https://www.books.com.tw/products/0010579897) 113 | * 課程照片：[連結](https://drive.google.com/drive/folders/1HM54EvTAxyJUGOHzVcupI_3v1s753dRt?usp=sharing) 114 | 115 | ## 關於本攻略 116 | 117 | 本攻略由 Star Rocket 三創育成基金會整理分享，採用[姓名標示-相同方式分享 4.0 國際 (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/deed.zh_TW)授權。 118 | 119 | --------------------------------------------------------------------------------