SemesterSpring Semester, 2020
DepartmentInternational Doctor Program in Asia-Pacific Studies, First Year International Doctor Program in Asia-Pacific Studies, Second Year International Doctor Program in Asia-Pacific Studies, Third Year
Course NameData-Driven Decision Making
InstructorLIAO HSIN-CHUNG
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule

 



 


































































































































































週次



Week



課程主題



Topic



課程內容與指定閱讀



Content and Reading Assignment



教學活動與作業



Teaching Activities and Homework



學習投入時間



Student workload expectation



課堂講授



In-class Hours



課程前後



Outside-of-class Hours



2/17



Introduction and Course Overview


   

3



1



2/24



The Big Promise of Big Data






    1. Pentland, A. (2015). Social Physics: How social networks can make us smarter. Penguin. CH1

    2. Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH1

    3. The Age of Big Data: New York Times






  1. Give examples of public organizations using data to enhance performance.

  2. Define “big” data.

  3. Identify Indicators



3



3



3/2



The Challenge of Big Data: Information Blindness




  1. Duhigg, C. (2016). Smarter faster better: The secrets of being productive. Random House. CH8 pp 238-247, 252-267

  2.  Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH2 the rise of big crisis data pp 25-31

  3. Gugerty, M. K., & Karlan, D. (2018). Ten reasons not to measure impact—And what to do instead. Stanf. Soc. Innov. Rev.




  1. Identify challenges of using big data for management.



3



3



3/9



The Challenges of Big Data: Organizational Change




  1. Desouza, K. C., & Smith, K. L. (2014). Big data for social innovation. Stanford Social Innovation Review, 2014, 39-43.

  2. Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard business review, 90(10), 78-83.




  1. Identify challenges of using big data for management.



3



3



3/16



Challenges of Big Data: Ethics and Privacy




  1. O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books. Introduction pp 1-13




  1. Identify challenges of using big data for management.



3



3



3/23



Collecting Group Data




  1. Pentland, A. (2015). Social Physics: How social networks can make us smarter. Penguin. CH5 observing people in organizations

  2. Eagle, N., & Greene, K. (2014). Reality mining: Using big data to engineer a better world. MIT Press. CH3 gathering group data




  1. Give examples of new types of sensors and crowd-sourcing tools that can be used to generate data for organizations.

  2. Identify ways in which this data has been used to improve organizational performance or government transparency and accountability.

  3. Discuss new techniques that help turn raw data from crowd-sourcing platforms and satellite images into structured data that can be used for statistical analysis.



3



3



3/30



Using Administrative Data




  1. Eagle, N., & Greene, K. (2014). Reality mining: Using big data to engineer a better world. MIT Press. CH7 mobile and internet data




  1. The analysis of 1999 Citizen Hotline Data



3



3



4/6



Harnessing Social Media Data




  1. Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH3 crowd computing social media

  2. Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH5 artificial intelligence for disaster response




  1. Discuss types of social media data that can be helpful during disaster responses.



3



3



4/13



Final Project Proposal Discussion



None



Individual Discussion in Office



3



3



4/20



Remote Sensors




  1. Eagle, N., & Greene, K. (2014). Reality mining: Using big data to engineer a better world. MIT Press. CH5 urban analytics: traffic data, crime stats, and closed-circuit cameras

  2. Pentland, A. (2015). Social Physics: How social networks can make us smarter. Penguin. CH8 sensing cities




  1. Identify ways that remote sensors are making cities smart and saving money.



3



3



4/27



Challenges of Data Quality




  1. Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH2 the rise of big crisis data pp 31-47

  2. Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH7 verifying big crisis data via crowd computing

  3. Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH8 verifying big crisis data via artificial intelligence


 

3



3



5/4



Static Data Visualization




  1. Few, Stephen. 2012. Show Me the Numbers: Designing Tables and Graphs to Enlighten. 2nd ed. USA: Analytics Press.

  2. Tufte, Edward R. 1983. The Visual Display of Quantitative Information. Graphics Press. Chapter 2, "Graphical Integrity".




  • GeoDa Practice 1



3



3



5/11



Volunteered Geographic Information (VGI)




  1. Jiang, Bin, and Jean-Claude Thill. 2015. “Volunteered Geographic Information: Towards the Establishment of a New Paradigm.” Computers, Environment and Urban Systems, Special Issue on Volunteered Geographic Information, 53 (September): 1–3.

  2. Zook, Matthew, Mark Graham, Taylor Shelton, and Sean Gorman. 2010. “Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake.” World Medical & Health Policy 2 (2): 6–32.




  • GeoDa Practice 2



3



3



5/18



Participatory Mapping




  1. Parker, Brenda. “Constructing Community through Maps? Power and Praxis in Community Mapping.” Professional Geographer, 58:4, (2006): 470-484

  2. Norwood, Carla, and Gabriel Cumming. "Making maps that matter: Situating GIS within community conversations about changing landscapes." Cartographica: The International Journal for Geographic Information and Geovisualization 47.1 (2012): 2-17.




  • GeoDa Practice 3



3



3



5/25



Final Project Workshop



None



Open Lab



3



3



6/1



Final Project Presentation


 

Potluck (Drinks and Snacks)



3



3



6/8



Final Project Presentation


 

Potluck (Drinks and Snacks)



3



3



6/15



Final Exam


 

Take-Home Final Exam



0



6




 


Teaching Methods
Teaching Assistant
Requirement/Grading

The final semester grade will be computed as:




  • 10% for the oral presentation of the final project

  • 40% for the  final project (3000-5000 words)  

  • 15% for the take-home final exam

  • 15% for the assignment

  • 10% for the classroom discussion

  • 10% for the participation


Textbook & Reference
Urls about Course
Attachment