課程教學目的說明:內容融合了實務應用、學生視角與業界趨勢，也兼顧親和力與專業感：
課程教學目的說明：
### **資料怎麼來？怎麼整理？怎麼用？來，一次學會！**
在這個「數據當道」的時代，我們經常聽到「大數據」、「AI」、「資料分析師」這些熱門詞彙，但回歸根本，資料分析的第一步是什麼？不是畫圖，也不是跑機器學習模型，而是——**搞懂資料從哪裡來、長什麼樣子、怎麼清理、怎麼查出我要的東西。**
這門課程的目的很簡單，就是帶你從零開始，一步步學會如何使用 **SQL** 和 **Python**，搞定資料的存取與清洗工作。不需要你有程式設計背景，也不要求你是資料庫高手，只要你對資料有一點點興趣，我們就能帶你走過這段從混亂資料到乾淨資訊的旅程！
✅ 我們想教會你的，不只是語法，而是「資料處理的邏輯力」！
SQL 是資料世界的萬用工具，是「資料庫的語言」，能幫助我們快速撈出成千上萬筆資料中的你要的「那幾筆」，就像超市結帳時快速掃描條碼那樣俐落。而 Python 是資料分析師的瑞士刀，能處理、清洗、轉換、合併、分析、可視化，一條龍完成資料的加工流程。
在這門課中，我們不只會教你「語法怎麼寫」，更重要的是讓你理解——
* 資料為什麼要正規化？
* 哪些資料該進資料庫、哪些該進資料表？
* 為什麼一張報表出不來，可能是你 JOIN 錯了？
* 清洗資料有什麼潛在風險？
* 資料欄位不乾淨，AI 跑起來會出什麼問題？
透過一個個實務案例，我們會讓你體驗：**從資料庫拉出資料 → 用 Python 清洗整理 → 輸出成漂亮的報表或供後續模型使用** 的完整流程。
教你用的工具，不是冷冰冰的理論，而是業界真的在用的
* **SQLite：** 輕巧又實用的本地資料庫，最適合初學者練習用
* **SQL 語法：** 讓你能查資料、更新資料、刪除資料、合併資料、做報表
* **Python pandas：** 幫你一鍵清洗資料、找出缺值、重組欄位、合併表格
* **Jupyter Notebook：** 實作與紀錄二合一，寫報告也能直接交！
我們甚至會示範怎麼結合 Python 與 SQL，讓你寫程式的同時就能撈資料表、跑查詢、整理資料，這可是資料工程師與分析師每天都在做的事！
你會做出什麼？
* 一個可操作的資料管理系統（如：學生系統、圖書系統）
* 幾十萬筆資料的清洗流程（包含錯字、缺值、日期轉換等）
* 自動化資料查詢報表（如每日銷售總表、成績分布表等）
* 用 SQL + pandas 寫一個「從資料庫到報表」的 mini pipeline
學完這門課，你將具備什麼能力？
1. **能看懂與撰寫 SQL 查詢語法，並懂得如何應用在資料篩選與分析上**
2. **能使用 Python 處理結構化資料（表格）、完成清洗、合併與分析任務**
3. **能設計並管理簡易資料庫系統，具備 CRUD 能力**
4. **能理解資料流轉流程，為未來學習資料科學、機器學習做好準備**
5. **具備使用資料解決問題的能力，而不只是「會寫程式」**
適合修課的你：
* 想進資料科學或 AI 領域，卻對資料庫一知半解的你
* 曾經查詢資料卡關，總是被資料庫嚇退的你
* 想強化實務資料處理能力，並整合 Python + SQL 工具箱的你
* 修過 Python 或資料分析入門課，想更進一步了解資料後台處理的你
一句話總結這門課：
這不是一門只有語法的課，而是一門教你如何駕馭資料、清洗資料、轉化資料，並用資料說故事的實戰型課程！**
Explanation of course teaching: The content combines practical applications, student perspectives and professional trends, and also provides a harmonious and professional sense:
Course tutorial purpose description:
### **How does the data come about? How to organize it? How to use it? Come, a lesson! **
In this era of "data wise", we often hear these popular words such as "big data", "AI", and "data analyst", but when we look back, what is the first step in data analysis? It's not a picture, nor a machine learning model, but - **know where the data comes from, what it takes, how to clean it up, how to find out what I want. **
The purpose of this course is very simple, which is to take you from scratch and learn step by step how to use **SQL** and **Python** to handle data access and cleaning. You don’t need to have a background in programming, nor do you need to be a database expert. As long as you have a little interest in the data, we can take you through this journey from confusing data to clean information!
✅ What we want to teach you is not just syntax, but the "logic power of data processing"!
SQL is a tool in the data world, the "database language", which can help us quickly extract the "those pens" you want from thousands of documents, just like quickly scanning bar codes when you register a supermarket. Python is a Swiss knife of a data analyst. It can process, clean, convert, merge, analyze, and visualize it, and complete the processing process of data in one row.
In this class, we will not only teach you "how to write syntax", but more importantly, let you understand-
* Why should data be formalized?
* What data should be entered into the database and which data tables should be entered?
* Why can't a report come out? Maybe your JOIN is wrong?
* What are the potential risks of cleaning data?
* If the data field is not clean, what problems will happen to AI when it runs?
Through a practical case, we will give you a complete process of pulling out data from the database → Cleaning and organizing in Python → Output into a beautiful report or for a continuous model to use **.
The tools you teach you are not cold theory, but are really used by the industry.
* **SQLite: ** A light and practical local database, most suitable for beginners to practice
* **SQL Syntax: ** Let you check, update, delete, merge data, and make reports
* **Python pandas: ** Help you clean data, find missing values, regroup fields, and merge tables
* **Jupyter Notebook: ** Two-in-one practice and record, you can submit it directly if you write a report!
We even show how to combine Python and SQL so that you can write programs while you can edit data sheets, search and organize data. This is what data engineers and analysts do every day!
What would you do?
* An operational data management system (such as: student system, book system)
* The cleaning process of hundreds of thousands of document data (including errors, missing values, date conversion, etc.)
* Automatic data query report (such as daily sales total, performance distribution table, etc.)
* Use SQL + pandas to write a mini pipeline that "from database to report"
After studying this class, what abilities will you have?
1. **Able to understand and write SQL query syntax and know how to apply it to data screening and analysis**
2. **Able to use Python to process structural data (tables), complete cleaning, merging and analysis tasks**
3. **Ability to design and manage simple database systems, with CRUD capabilities**
4. **Able to understand the data flow conversion process and prepare for future data science and machine learning**
5. ** Have the ability to use data to solve problems, not just "write programs"**
Suitable for you in the course:
* You want to enter the data science or AI field and have only a little understanding of the data library
* You have been searching for data card, but you are always scared by the database
* You want to strengthen your practical data processing capabilities and integrate Python + SQL toolbox
* You have studied Python or data analysis courses and want to further understand the background processing of data
A summary of this lesson in one sentence:
This is not a class with only syntax, but a warlike course that teaches you how to learn, clean, convert data, and tell stories with data! **
講義
Talk
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課程參與小組討論與互動課程參與小組討論與互動 Course participation and group discussion and interaction |
10 | |
| 期末報告期末報告 Final report |
30 | |
| 作業與討論點名作業與討論點名 Business and discussion point names |
30 | |
| 期中報告期中報告 Midterm Report |
30 |
