教學大綱 Syllabus

科目名稱：資料模式

Course Name: Data Models
  • 學年學期：112-1 Fall Semester, 2023
  • 科目代碼：356021001 Course No.356021001

修別：必

Type of Credit: Required

3.0

學分數

Credit(s)

50

預收人數

Number of Students

課程資料Course Details
  • 開課單位：資管碩一 Course Department:Management Information Systems/M/1
  • 授課老師：洪為璽 Instructor: HUNG WEI-HSI
  • 先修科目：[ 資料庫管理(單學期)(修習且及格)]Prerequisite([])
  • 上課時間：四D56 Session: thu13-16

課程簡介Course Description

Nowadays, we are in a very technology-centered world. Organizations are developing new systems every day. However, we have seen many unsuccessful cases in the IT development projects. One of the key reasons is the absence of appropriate data modelling. There is no common language between those system developers, and this is where the data modeler comes in. Also, some organizations may develop certain data model that are similar to others, which means they don’t have to develop it from scratch. Thus, this course tends to teach students how to conduct data modelling in order to save organizations’ time and money by leveraging the use of common or universal database structures.

核心能力分析圖 Core Competence Analysis Chart

能力項目說明

    課程目標與學習成效Course Objectives & Learning Outcomes

    This Data Models (DM) course tends to achieve following objectives:

    • Students can understand both academic theories and practical implications of DM.
    • Students can understand DM methods through a series of teaching activities.
    • Students can learn DM knowledge from real-world cases through in-class discussions.
    • Students can learn how to apply DM methods to analyze various kinds of processes in organizations.

    每周課程進度與作業要求 Course Schedule & Requirements

    教學週次Course Week 彈性補充教學週次Flexible Supplemental Instruction Week 彈性補充教學類別Flexible Supplemental Instruction Type

    週次

    Week

    課程主題

    Topic

    課程內容與指定閱讀

    Content and Reading Assignment

    教學活動與作業

    Teaching Activities and Homework

    學習投入時間

    Student workload expectation

    課堂講授

    In-class Hours

    課程前後

    Outside-of-class Hours

    1

    Course Overview & Grouping

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    2

    Introduction

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    3

    Data Modeling using E/R diagrams 1

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    4

    Data Modeling using E/R diagrams 2 (IDEF1x)

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    5

    Data Warehousing

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    6

    On-line Analytic Processing (OLAP)

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    7

    Design Practice: Star Schema Designs and UDM

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    8

    Case Study 1

    Speech

    Lecture Discussions

    3

    4

    9

    Case Study 2

    Speech

    Lecture Discussions

    3

    4

    10

    Midterm examination

    Textbook & Reading Materials

    Examination

    3

    4

    11

    Final Project Discussions

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    12

    Field Case Investigation 1

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    13

    Field Case Investigation 2

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    14

    Field Case Investigation 3

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    15

    Final Project Preparation

    Textbook & Reading Materials

    No Class

    3

    4

    16

    Final Project Preparation

    Textbook & Reading Materials

    No Class

    3

    4

    17

    Final Project Presentation 1

    Textbook & Reading Materials

    Lecture Discussions

    3

    4

    18

    Final Project Presentation 2

    Textbook & Reading Materials

    Discussions

    3

    4

    授課方式Teaching Approach

    50%

    講述 Lecture

    25%

    討論 Discussion

    25%

    小組活動 Group activity

    0%

    數位學習 E-learning

    0%

    其他： Others:

    評量工具與策略、評分標準成效Evaluation Criteria

    Midterm Examination: 50%

    Final Project Presentation: 25% (Oral Presentation. Group based. The class will be divided into 6 subgroups. More details will be announced in Week 2)

    AWS Data Engineering Activities and Attendance: 15%

    Class Participation: 10%

    指定/參考書目Textbook & References

    • The Data Model Resource Book Revised Edition Volume 1 by Len Silverston, Wiley Computer Publishing, John Wiley & Sons, Inc. New York. 2001
    • Data Modeling Fundamentals: A Practical Guide for IT Professions by Paulraj Ponniah, Wiley-Interscience, John Wiley & Sons, New Jersey, 2007
    • Draft Federal Information Processing Standards Publication 183, 1993 December 21

    已申請之圖書館指定參考書目 圖書館指定參考書查詢 |相關處理要點

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