Summary of GDRR 515 - Data Science Applications in DRR

GDRR 515 - Data Science Applications in DRR

The data science course aims to provide tools and techniques to measure and understand risks, vulnerabilities and resilience in DRR that is critical for disaster response and recovery decision-making. It aids in comprehending how disaster risk may evolve in the future, as well as how technology may be used to improve disaster preparedness. This course blends the core elements of data science namely big data, machine learning and business intelligence which are helpful in finding out patterns from raw data which can be used in the formation of big decisions in the field of DRR.

Contact Hours

56 hours
1 meeting per week 4 hours per meeting
14 weeks

Course Learning Objectives

At the end of the course, the students shall be able to:

CLO 1: Apply statistical computing and modeling in measuring and understanding risks, vulnerabilities and resilience in DRR.
CLO 2: Critically evaluate the application of technology in DRR
CLO 3: Capture, transform and translate DRR data for decision making.

References

Social Sensing and Big Data Computing for Disaster Management, Zhenlong Li, Qunying Huang, Christopher T. Emrich, Copyright Year 2021

Machine Learning for Disaster Risk Management, Deparday, V. et al., 01 Dec 2018

Machine Learning Simplified, A Gentle Introduction to Supervised Learning, Andrew Wolf, 2021

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions 1st Edition. Matt Taddy, 2021

Call Number	Description
G-ICS 006.310151 L721 2019	Little, Max A. Machine learning for signal processing : data science, algorithms, and computational statistics. First Edition. Oxford, U.K.: Oxford University Press, ©2019.
G-ICS 519.55 W4361 2018	Weiss, Christian H. An introduction to discrete-valued time series. Hoboken, N.J.: John Wiley & Sons Ltd, ©2018.
ICS 005.74 L54 2018	Lemahieu, Wilfried. Principles of database management : the practical guide to storing, managing and analyzing big and small data. First edition. New York, NY : Cambridge University Press, 2018.
ICS 006.31 C4289 2018	Chew, Xuanyi. Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics application in Go. Birmingham : Packt Publishing, c2018.
ICS 006.31 G897 2017	Géron, Aurélien. Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems. First edition. Sebastopol, CA : O'Reilly Media, ©2017.
ICS 006.31 H79 2019	Hosanagar, Kartik. A human's guide to machine learning : how algorithms are shaping our lives and how we can stay in control. New York, New York : Viking, @2019.
ICS 006.31 K28 2015	Kelleher, John D., 1974-. Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies. Cambridge, Massachusetts : The MIT Press, ©2015.
ICS 006.31 N175 2019	Nash, Margaret. Understanding machine learning. New York : Clanrye International, ©2019.

https://opendri.org/wp-content/uploads/2021/06/ResponsibleAI4DRM.pdf