1131學期 課程基本資料/Course Information
|
系所 / 年級 |
財金系 2年級 |
課號 / 班別 |
86U00202 / A |
學分數 |
3學分 |
選 / 必修 |
選修 |
科目中文名稱 |
金融大數據分析 |
科目英文名稱 |
Financial Big Data Analytics |
主要授課老師 |
簡智崇 |
開課期間 |
一學年之上學期 |
人數上限 |
70 人 |
已選人數 |
58 人 |
課程種類 |
一般課 |
課程類別 |
系定 |
學程 |
系自由選修課程 |
全英授課 |
N |
起始週 / 結束週 / 上課地點 / 上課時間
|
第1週 / 第18週 / M306 / 星期3第2節
第1週 / 第18週 / M306 / 星期3第3節
第1週 / 第18週 / M306 / 星期3第4節
請各位同學遵守智慧財產權觀念;請勿非法影印。
Please observe Intellectual Property Rights (IPR), not to make illegal copies.
|
教學綱要/syllabus |
第一部分/Part I(※依課程委員會審議之內容決議填入) |
一、教學目標所要達成之能力培養項目: [依據課程委員會審議通過之課程與基本素養/核心能力關聯表填寫] |
Item |
基本素養/核心能力 Core Literacy/Core Competencies |
相關性 Relevance |
高度相關 |
中度相關 |
1 |
專業能力 |
|
. |
2 |
溝通能力 |
. |
. |
3 |
分析與問題解決能力 |
|
. |
4 |
倫理觀 |
. |
. |
5 |
國際觀 |
. |
. |
SDGs Item |
SDGs目標 SDGs goal |
SDGs描述 SDGs description |
無相關項目 No related items |
|
二、教學目標 (Objective) |
1.認知面:[使學生理解、應用、分析、綜合、比較、推論、評估本課程之理論與概念]:
Data scientist has been called “the sexiest job of the 21st century,” presumably by
someone who has never visited a fire station. Nonetheless, data science is a hot and
growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly
prognosticating that over the next 10 years, we’ll need billions and billions more data
scientists than we currently have.
2.技能面[使學生能獲得運用與實做本課程理論與概念之技巧]:
But what is data science? After all, we can’t produce data scientists if we don’t know what
data science is. According to a Venn diagram that is somewhat famous in the industry, data
science lies at the intersection of:
1. Hacking skills
2. Math and statistics knowledge
3. Substantive expertise
3.情意面[能引發學生對本課程之興趣,激發學生學習動機,增加觸類旁通與自主學習]:
The goal of this course is to help you develop the hacking skills that you’ll need to get started doing data science. And my goal is to help you get
comfortable with the mathematics and statistics that are at the core of data science.
|
三、符合教學目標之課程內容設計 |
The best way to learn hacking skills is by
hacking on things. By this course, you will get a good understanding of the way we
hack on things, which may not necessarily be the best way for you to hack on things. You
will get a good understanding of some of the tools I use, which will not necessarily be the
best tools for you to use. You will get a good understanding of the way I approach data
problems, which may not necessarily be the best way for you to approach data problems. Similarly, the best way to learn mathematics is by doing mathematics. This means that, where appropriate, we will dive into mathematical
equations, mathematical intuition, mathematical axioms, and cartoon versions of big
mathematical ideas.
Throughout it all, I also hope to give you a sense that playing with data is fun, because,
well, playing with data is fun! |
四、先修科目 (Pre Course) |
Not required |
第二部分/Part II |
一、多元教學方法 (Teaching Method) |
|
二、多元教學方法與教育目標的連結 |
|
三、參考書目 (Reference) [符合教學目標之參考書目] |
Enterprise Guide 1: Querying and Reporting Applied Analytics Using SAS Enterprise Miner. SAS institute offer the related material for academic purpose.
|
|
四、教學進度 (Syllabi) [符合教學目標之教學進度] |
教學進度與何種基本素養/核心能力有關?
|
五、多元評量方法 (Evaluation) [所勾選評量方法之評分加總為100分] |
|
六、多元評量方法與教育目標的連結 |
|
七、講義位址(http://) |
|
八、遠距教學 Distance Learning |
|