Type of Credit: Required
Credit(s)
Number of Students
As the demand for robust data warehousing and advanced analytics solutions increases, this course will equip students with the essential skills to leverage BigQuery for data analysis, management, and visualization and develop and deploy machine learning models using BigQuery ML. Through lectures, labs, homework assignments, and a capstone project, students will engage with theoretical concepts and hands-on practical applications. By the end of this course, students will be proficient in navigating and optimizing the use of Google Cloud's powerful data analytics tools, preparing them for roles in data science, business intelligence, and machine learning.
能力項目說明
By the end of this course, students will be able to:
These goals ensure that students gain a strong foundation in both technical skills and practical applications, readying them for advanced roles in data analytics and machine learning.
| 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 |
課程主題 |
課程內容與指定閱讀 |
教學活動與作業 |
|
1 9/12 |
Introduction to BigQuery |
1. Course overview and objectives. 2. Introduction to cloud databases and BigQuery's unique features. |
|
|
2 9/19 |
Setting Up and First Queries |
1. Setting up Google Cloud Platform and BigQuery. 2. Executing basic SQL queries. |
|
|
3 9/26 |
Advanced SQL Features |
Data filtering, sorting, and advanced SQL functions. |
Homework 1: Basic SQL queries on sample datasets. |
|
4 10/3 |
Data Loading and Exporting |
1. Importing and exporting data to and from BigQuery. 2. Understanding data formats and best practices. |
|
|
5 10/10 |
National Holiday |
No Class |
|
|
6 10/17 |
Data Management |
1.Table partitioning and clustering. 2.Best practices for data storage and cost optimization. |
Homework 2: Implement partitioning and analyze performance.
|
|
7 10/24 |
BigQuery Data Analysis Techniques |
Advanced analytical functions. Practical data analysis scenarios. |
|
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8 10/31 |
Visualization and External Tools |
Integrating BigQuery with Google Data Studio and other BI tools. |
Homework 3: Create a dashboard using Data Studio. |
|
9 11/7 |
Security and Compliance |
Access controls, security settings, and compliance within BigQuery. |
|
|
10 11/14 |
Performance Optimization |
Query performance tuning and cost-effective practices. |
Homework 4: Optimize a set of provided slow-running queries
|
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11 11/21 |
Exam |
Midterm Exam |
|
|
12 11/28 |
Introduction to BigQuery ML |
Overview of machine learning in BigQuery. Creating and training simple models. |
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|
13 12/5 |
Model Building and Evaluation |
Techniques for model evaluation and tuning. Building classification and regression models.
|
Homework 5: Build and evaluate classification and regression models. |
|
14 12/12 |
ML Models |
Introduction to time series forecasting and clustering in BigQuery ML. |
|
|
15 12/19 |
Deep Learning and Neural Networks |
Basics of using neural networks within BigQuery ML. |
|
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16 12/26 |
Final Exam |
Exam |
|
|
17 1/2 |
Final Project |
Presentation |
|
|
18 1/9 |
Final Project |
Presentation |
|
Homework 40%
Midterm Exam 20%
Final Exam 20%
Final Project 20%
Google's official documentation for BigQuery and BigQuery ML
| 書名 Book Title | 作者 Author | 出版年 Publish Year | 出版者 Publisher | ISBN | 館藏來源* | 備註 Note |
|---|
https://cloud.google.com/bigquery/docs https://cloud.google.com/bigquery/docs/bqml-introduction