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
109-1 
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201 
備註
初選不開放。限圖資、資管。選修者需符合選課條件(9/7起公告於開課教師系網個人頁面)並於第1週準時出席。與孔令傑共授
總人數上限:20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 which involves collect, cleanse, analyze, visualize, and finally select the optimal decision to suggest an action.  
課程要求
N/A 
預期每週課後學習時數
 
Office Hours
另約時間 
參考書目
[Ber] Berinato, S. (2016). Good charts: The HBR guide to making smarter, more persuasive data visualizations. Harvard Business Review Press.
[Cai1] Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders. (Call no. T385 C33875 2013)
[Cai2] Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.
[Cam] Camões, 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)
[Kna] Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley. (Call no. QA76.9.I52 K64 2015)
[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)
[May] Mayer-Schönberger, 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)
[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) 
指定閱讀
N/A 
評量方式
(僅供參考)
 
No.
項目
百分比
說明
1. 
Weekly Labs and Participation 
45% 
The overall participation grade includes weekly activities, weekly Labs, and hand-on practices. 
2. 
Midterm Exam 
25% 
 
3. 
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.  
 
課程進度
週次
日期
單元主題
第1週
9/16  Course introduction 
第2週
9/23  Understand digital data: numbers, measurement, and variables; Recap: Descriptive statistics 
第3週
9/30  Data visualization 1– Visualization principles and critics  
第4週
10/07  Data visualization 2– Effective data visualization 
第5週
10/14  Data wrangling 1– Code-free data preparation and cleansing 
第6週
10/21  Data wrangling 2– Lab: Tableau Prep Builder 
第7週
10/28  Explanatory Analytics – Regression 
第8週
11/04  Data visualization 3– Data storytelling and decision making 
第9週
11/11  Midterm 
第10週
11/18  Predictive Analytics – Regression  
第11週
11/25  Predictive Analytics – Classification 
第12週
12/02  Thematic visualization: Network concepts and visualization (**class extended to 6PM) 
第13週
12/09  TP checking point; project debriefing @ Office/Online (TBA) 
第14週
12/16  TP checking point; project debriefing @ Office/Online (TBA) 
第15週
12/23  TP presentations 
第16週
12/30  TP presentations 
第17週
1/06  Debriefing