Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	365939	Introduction to Big Data Analytics	1	3.0	3	★	Jenq-Haur Wang	4CSIE2	40	0

Course Plan：

Instructor	Jenq-Haur Wang　Office Hours
Email	jhwang@ntut.edu.tw
Last Updated	2026-06-03 12:55:42
Course Objective	本課程為大學部的巨量資料分析導論基礎課程，主要介紹資料前處理、資料探勘概念、關聯分析、分類、分群、平行化程式設計、分散式計算、以及實際分析平台工具如: MapReduce, Spark等。除了基本概念的介紹外，透過程式練習以及期末專題，能讓學生深入理解巨量資料分析的基本原理以及實際工具的運用。
Course Schedule	Part I: Data mining concepts (8 wks) 1. Introduction to big data & preprocessing (1 wk) 2. Frequent pattern mining (2 wks) 3. Data classification (2 wks) 4. Data clustering (2 wks) 5. Midterm exam (1 wk) Part II: Distributed platforms & Parallel programming (4 wks) 1. Introduction to distributed platforms: Hadoop & Spark (1 wk) 2. Parallel programming paradigm & concepts (1 wk) 3. MapReduce programming: pairs & stripes, relational algebra (1 wk) 4. More examples: matrix multiplication, similarity (1 wk) Term project presentation (4 wks) Flexible Learning: (2 wks) 1. Group-based project implementation and discussions: for make-up homeworks, and extra bonus
Evaluation and grading policy	Homework assignments and programming exercises: ~40% Mid-term exam: ~25-30% Term project and presentation: ~30-35% Extra bonus: <=10% (for makeup homeworks in flexible learning)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes - Textbooks: 1. Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011. 2. Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, Morgan & Claypool Publishers, 2010. - References: 1. Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets, 2nd edition, Cambridge University Press, 2014. 2. Recent academic papers, tutorials, and slides. - Latest information available soon at: https://myweb.ntut.edu.tw/~jhwang/BDA/
The access to curricular consultation	1. 可以透過 e-mail 或 Microsoft Teams 裡的訊息聯絡到老師。 2. 助教可建立 Facebook 的課程社群，以供同學詢問作業或考試相關問題。
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	1. 課程會盡可能同步錄影，並於課後放上北科i學園，教材與相關訊息將公布在以下管道: 北科i學園、Teams、或課程網頁。 2. 所有修課同學將自動被加入課程Teams團隊。如果你是特殊情形加選，或是需要在開學後才能選課完成，目前仍未被加入本課程之Teams團隊，請來信: jhwang@ntut.edu.tw，並註明課程名稱與你的班級、姓名。 彈性學習: 一、類別 學生分組實作及討論 二、內容 (1)第 17-18 週:各組針對自選程式作業，可額外進行分組程式實作與討論 (2)第 18 週結束前:各組繳交額外程式作業 三、時數 共 6 小時 四、學習成果 第 18 週課程結束前各組須繳交額外完成之程式作業 五、評量比例 17-18 週額外程式作業:不超過10% (總分額外加分)