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！**

Description of the course teaching purpose: The content integrates practical applications, student perspectives and industry trends, while also taking into account both affinity and professionalism:
Description of course teaching purpose:

### **How ​​do you get the information? How to organize it? How to use it? Come, learn it once! **

In this era of "data rules", we often hear buzzwords such as "big data", "AI", and "data analyst", but back to the basics, what is the first step in data analysis? It’s not about drawing pictures or running machine learning models, but—**understanding where the data comes from, what it looks like, how to clean it, and how to find out what I want. **

The purpose of this course is very simple. It is to help you learn how to use **SQL** and **Python** step by step from scratch to access and clean data. You don’t need to have a programming background, nor do you need to be a database expert. As long as you have a little bit of interest in data, we can take you through this journey from chaotic data to clean information!

✅ What we want to teach you is not just grammar, but "the logic of data processing"!

SQL is a universal tool in the data world and the "language of the database". It can help us quickly retrieve the "those items" you want from thousands of pieces of data, just like quickly scanning the barcode at the supermarket checkout. Python is the Swiss knife of data analysts. It can process, clean, convert, merge, analyze, and visualize data to complete the data processing process in one step.

In this course, we will not only teach you "how to write grammar", but more importantly, let you understand——

* Why should data be formalized?
* Which data should be entered into the database and which should be entered into the data table?
* Why can't a report come out? Maybe it's because you made a wrong JOIN?
* What are the potential risks of cleaning data?
* If the data field is not clean, what problems will occur when the AI ​​runs?

Through practical cases, we will let you experience the complete process of: **pulling data from the database → cleaning and organizing with Python → outputting it into a beautiful report or for use in subsequent models**.

The tools we teach you are not cold theories, but are actually used by the industry.

* **SQLite:** A lightweight and practical local database, most suitable for beginners to practice
* **SQL syntax:** Allows you to check data, update data, delete data, merge data, and make reports
* **Python pandas:** Helps you clean data, find missing values, reorganize fields, and merge tables with one click
* **Jupyter Notebook:** Implementation and recording in one, you can write reports and submit them directly!

We will even demonstrate how to combine Python and SQL, so that you can retrieve data tables, run queries, and organize data while writing programs. This is what data engineers and analysts do every day!

What will you do?

* An operational data management system (such as student system, library system)
* Cleaning process for hundreds of thousands of data (including typos, missing values, date conversion, etc.)
* Automated data query reports (such as daily sales summary table, performance distribution table, etc.)
* Use SQL + pandas to write a mini pipeline "from database to report"

What abilities will you have after completing this course?

1. **Be able to understand and write SQL query syntax, and know how to apply it to data screening and analysis**
2. **Ability to use Python to process structured data (tables), complete cleaning, merging and analysis tasks**
3. **Be able to design and manage a simple database system, with CRUD capabilities**
4. **Be able to understand the data transfer process and prepare for learning data science and machine learning in the future**
5. **Have the ability to use data to solve problems, not just "can write programs"**

The course is suitable for you:

* If you want to enter the field of data science or AI, but know little about databases?
* Have you ever been stuck in querying data and were always scared off by the database?
* If you want to strengthen your practical data processing capabilities and integrate the Python + SQL toolbox
* If you have taken an introductory course in Python or data analysis and want to know more about data background processing

To sum up this course in one sentence:

This is not a grammar-only course, but a practical course that teaches you how to control data, clean data, transform data, and use data to tell stories! **

講義

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