巨量資料分析導論

臺北科技大學 115-1 學期課程，課號 365939。

課程大綱

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。

課程進度

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

評量方式

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)

教材

【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 - 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/