教學大綱 Syllabus

科目名稱：資料模式

Course Name: Data Models
  • 學年學期：114-1 Fall Semester, 2025
  • 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：向倩儀 Instructor: HSIANG CHIEN-YI
  • 先修科目：[ 資料庫管理(單學期)(修習且及格)]Prerequisite([])
  • 上課時間：一D56 Session: mon13-16

課程簡介Course Description

As the demand for robust cloud computing, scalable data warehousing, and advanced analytics solutions continues to grow, this course provides a comprehensive journey through the Google Cloud Platform (GCP) ecosystem. Students will begin by mastering the fundamentals of cloud infrastructure, including Compute Engine (VMs) and Kubernetes (GKE), thereby establishing a solid foundation in cloud-based computation and container orchestration.

From there, the course transitions into real-time messaging with Pub/Sub and data processing pipelines using Dataflow and Dataproc, enabling students to manage both streaming and batch data at scale. Learners will then explore BigQuery, GCP's serverless enterprise data warehouse, gaining skills in data management, SQL analytics, and performance tuning.

The curriculum also covers BigQuery ML, where students will develop and deploy machine learning models directly within BigQuery using SQL, bridging the gap between analytics and predictive modeling. Additional topics include Spanner for globally consistent relational databases, Bigtable for large-scale NoSQL workloads, and BigLake for unified data lake analysis. To visualize and communicate insights, students will use Looker Studio to create interactive dashboards and reports. Finally, learners will explore the integration of Gemini, Google's advanced multimodal AI model, applying generative AI capabilities to data analysis, application development, and business intelligence.

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

    After completing this course successfully, students will have the ability to:

    1. Deploy and manage cloud infrastructure: Confidently create virtual machines, orchestrate containers, and understand the fundamentals of cloud operations.
    2. Build and optimize data pipelines: Integrate Pub/Sub, Dataflow, and Dataproc to process both streaming and batch data efficiently.
    3. Leverage BigQuery for analytics: Manage datasets, write complex SQL queries, optimize performance, and extract actionable insights from large-scale data.
    4. Apply machine learning with BigQuery ML: Develop, train, and evaluate predictive models using SQL, and apply them to real-world datasets.
    5. Utilize advanced data services: Design solutions using Spanner for relational databases, Bigtable for high-volume NoSQL workloads, and BigLake for unified analytics across storage systems.
    6. Create data-driven dashboards: Communicate insights effectively using Looker Studio to build interactive and visually compelling reports.
    7. Integrate AI with Gemini: Apply multimodal AI capabilities to enrich analytics, automate workflows, and support intelligent business applications.
    8. Complete a capstone project: Demonstrate mastery of GCP's analytics and AI tools by designing an end-to-end solution that integrates infrastructure, data processing, machine learning, and visualization.

     

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

    週次

    課程主題

    課程內容與指定閱讀

    教學活動與作業

    1

    9/1

    Introduction to data models, cloud computing, and data engineering
    1. What are Data Models
    2. Introduction to Data Engineering on Google Cloud Platform

     

    2

    9/8

    VM, GKE, and Pub/Sub
    1. VM: Virtual machines on the cloud, a basic building block for compute
    2. Kubernetes (GKE): Container orchestration for scalable apps
    3. Pub/Sub: Messaging service for real-time event streaming

    Homework 1:

    Qwiklab - Create a Virtual Machine

    Qwiklab - Google Kubernetes Engine

    3

    9/15

    Dataflow & Dataproc
    1. Dataflow: Serverless data processing for batch and stream pipelines
    2. Dataproc: Managed Hadoop/Spark cluster for big data jobs

    Homework 2: Qwiklab -Pub/Sub

    Qwiklab - Dataflow

    Qwiklab - Dataproc

    4

    9/22

    Data Warehouse

    BigQuery: Serverless data warehouse for fast SQL analytics.

    Homework 3: BigQuery Advanced SQL function 

    5

    9/29

    National Holiday

    No Class

     

    6

    10/6

    National Holiday

    No Class

     

    7

    10/13

    BigTable
    1. High-Throughput BigQuery and Bigtable Streaming Features
    2. NoSQL database for large-scale, low-latency workloads

    Homework 4: Qwiklab - BigTable

    8

    10/20

    Data Lake

    BigLake: Unified data lake engine to query across storage systems

    Homework 5: Qwiklab - BigLake

    9

    10/27

    Exam

    Midterm Exam

     

    10

    11/3

    Spanner
    1. OLTP
    2. Globally distributed SQL database with strong consistency.

    Homework 6: Qwiklab - Spanner

    11

    11/10

    BigQuery ML

    BigQuery ML: Build and run machine learning models directly in BigQuery using SQL.

    Homework 7: BigQuery ML taxi fare

    12

    11/17

    Looker Studio

    Looker Studio: Visualization and dashboard tools to turn data into interactive reports.

    Homework 8: Looker visualization

    13

    11/24

    Gemini
    1. Google Cloud’s new GenAI model family
    2. Multimodal AI model for text, code, image, and data intelligence in Cloud services.

    Homework 10: Qwiklab – Gemini API

    14

    12/1

    Final Project

    Group Presentation

     

    15

    12/08

    Final Project

    Group Presentation

     

    16
    12/15

    Exam

    Final Exam

     

    授課方式Teaching Approach

    50%

    講述 Lecture

    10%

    討論 Discussion

    10%

    小組活動 Group activity

    30%

    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

    Homework 50%

    Midterm Exam 15%

    Final Exam 15%

    Final Project 20%

    指定/參考書目Textbook & References

    Google’s official documentation for the Google Cloud Platform (GCP) ecosystem.

    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

    維護智慧財產權，務必使用正版書籍。 Respect Copyright.

    本課程可否使用生成式AI工具Course Policies on the Use of Generative AI Tools

    有條件開放使用:Any use of AI tools during examinations will result in an automatic score of zero. Conditional Permitted to Use

    課程相關連結Course Related Links

    https://cloud.google.com/bigquery/docs
https://cloud.google.com/bigquery/docs/bqml-introduction

    課程附件Course Attachments

    課程進行中，使用智慧型手機、平板等隨身設備 To Use Smart Devices During the Class

    其他:The use of smart devices in exams is strictly prohibited; any violations will result in a grade of zero. Other regulation

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