課程資訊
課程名稱
數位決策:資料視覺化與機器學習
Digital Decision Making: Data Visualization and Machine Learning 
開課學期
110-1 
授課對象
文學院  圖書資訊學系  
授課教師
鄭瑋 
課號
LIS5098 
課程識別碼
126 U1610 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期三7,8,9(14:20~17:20) 
上課地點
綜401 
備註
初選不開放。初選不開放。圖資、資管且曾修程式設計與統計課程者優先。見圖資系網鄭瑋教師頁面。與孔令傑共授。與孔令傑共授
總人數上限:18人 
 
課程簡介影片
 
核心能力關聯
核心能力與課程規劃關聯圖
課程大綱
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課程概述

This course will prepare undergraduate and graduate students to leverage digital data and make better-informed choices by creating effective visualizations and applying machine learning techniques. This course will take students through modules, which are aligned with the data analysis: collecting, cleansing, analyzing, visualizing (including information design, visual art, and cognitive principles), and making informed predictions and choices:

-Understand data: Concepts around digital data and how we can transform them into visualization
-Design principles for data visualization and storytelling
-Data wrangling: Data preparation and cleansing
-Data visualization tools and applications, e.g., Tableau and Gephi
-Explanatory analytics: Applications and model building for insight delivering with linear and logistic regression
-Predictive analytics: Applications and model building for prediction with regression and classification methods.

By theoretical explanations and practical examples, these lecture modules are designed to guide students through the key steps in an execution of a data analysis and storytelling term project which can contribute to a better decision-making process.
Besides the lectures, the course is also comprised of Lab assignments, office meet-ups, class activities, and a term project. The components of the term project include identifying needs, collecting, cleansing, processing, analyzing, visualizing, and communicating with digital data. 

課程目標
At the conclusion of this course, students will be able to
▪ Engage in academic and technical discussions on concepts and techniques related to digital data
▪ Describe different natures of data variables and their corresponding visualization solutions
▪ Adopt statistical and machine learning techniques as approaches to analyze data by theoretical explanations and practical examples
▪ Execute a data storytelling project involving collecting, cleaning, analyzing, visualizing, and selecting the optimal decision to suggest an action. 
課程要求
N/A 
Office Hours
另約時間 
參考書目
Recommended textbooks and chapters (not required)
1. [Ber] Berinato, S. (2016). Good charts: The HBR guide to making smarter, more persuasive data visualizations. Harvard Business Review Press.
2. [Cai1] Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders. (Call no. T385 C33875 2013)
3. [Cai2] Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.
4. [Cam] CamU+F5es, J. (2016). Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel. New Riders. (Call no. QC397.5.I53 C35 2016)
5. [Dua] Duarte, N. (2019). Data story: explain data and inspire action through story. Ideapress Publishing.
6. [Ell] Elliott, A. (2018). Is that a Big Number?. Oxford University Press.
7. [Kna] Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley. (Call no. QA76.9.I52 K64 2015)
8. [Lev] Levitt, S. D. and Dubner, S. J. (2009). Freakonomics: A Rogue Economist Explores The Hidden Side of Everything. New York : William Morrow. (HB74.P8 L479z 2006)
9. [May] Mayer-SchU+F6nberger, V. and Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston : Houghton Mifflin Harcourt. (QA76.9.D343 M396 2013)
10. [Wil] Wilke, C. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media. (Call no. QA76.9.I52 W55 2019) https://clauswilke.com/dataviz/ 
指定閱讀
待補 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Term Project (TP) 
35% 
The term project is designed for students to integrate and extend knowledge acquired throughout the course and to apply their training and knowledge in DDM. 
2. 
Midterm Exam 
25% 
 
3. 
Weekly Labs and Participation 
40% 
The overall participation grade includes weekly activities, weekly Labs, and hand-on practices. 
 
課程進度
週次
日期
單元主題
第1週
9/22  Course introduction 
第2週
9/29  Understand data: numeracy, measurement, and variables 
第3週
10/06  Data visualization 1– Visualization principles and critics 
第4週
10/13  Data visualization 2– Effective visualization: schools, colors 
第5週
10/20  Data wrangling – Exploring data visualization; Code-free data preparation and cleansing 
第6週
10/27  DDM workshops (TBA) 
第7週
11/03  Explanatory Analytics – Regression 
第8週
11/10  Predictive Analytics – Regression 
第9週
11/17  Mid-term (take home) 
第10週
11/24  Predictive Analytics – Classification 
第11週
12/01  Data visualization 3– Data storytelling and data dashboard 
第12週
12/08  Thematic visualization– Network concepts and visualization 
第13週
12/15  TP debriefing @ Office/Online (TBA) 
第14週
12/22  TP debriefing @ Office/Online (TBA) 
第15週
12/29  TP presentations (TBA) 
第16週
1/05  No class meeting- Debriefing