課程資訊
課程名稱
全球衛生資訊處理實務
Introduction to Data Processing in Global Health Practice 
開課學期
108-2 
授課對象
公共衛生學院  全球衛生碩士學位學程  
授課教師
洪 弘 
課號
MGH7023 
課程識別碼
853EM0230 
班次
 
學分
1.0 
全/半年
半年 
必/選修
選修 
上課時間
星期四8,9,10(15:30~18:20) 
上課地點
公衛214 
備註
本課程以英語授課。合授教師:Jenny Hsieh 時間&地點:請洽系所辦
限本系所學生(含輔系、雙修生)
總人數上限:20人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1082MGH7023_DP 
課程簡介影片
 
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課程概述

This course will introduce students to a range of practical tools that can be applied in the field of global health. The course is structured in three sections covering the process of data capture, basic data cleaning and manipulation, and finally communication of data through visualization and infographics.
(1) REDCap data collection: This part of the course will provide an introduction to Research Electronic Data Capture (REDCap), a web-based tool for electronic data capture which has been widely used by the international research community. These sessions will cover the process of creating a REDCap project, designing the data collection instrument, data management features in REDCap, enabling online surveys, project testing, real-time data collection, and usage of the REDCap mobile app on mobile phones or tablets. Hands-on training will be provided after the lecture.

(2) Data cleaning and manipulation in R: This part of the course aims to give a general introduction to R, an open-source programming language for data analysis and statistics. These sessions will provide an overview of basic features and fundamental concepts in R. Students will be taught the basics of reading, cleaning, and manipulating datasets. Common types of messy data and ways to tidy them will be discussed. These discussions will include small exercises in writing R code and preparing data for analysis. Messy datasets will be provided for practice purposes.

(3) Data visualization and infographics: This section will cover the principles of and various approaches to data visualization, as well as key steps in building an effective visual. Students will learn ways to translate data into easily digestible information for various audiences using the software they know (e.g. Microsoft PowerPoint). Following taught lectures, students will work through a guided example to develop a visualization for a targeted audience.

All sessions will involve lectures followed by demonstrations, interspersed with exercises to provide students with hands-on experience. There will be a Q&A session at the end of each session.

This course will be taught in English on 6 weeks (2/20、2/27、4/30、5/7、5/14、5/21).
 

課程目標
Upon completion of the course, students should be able to:
• have a baseline competency in REDCap
• create and design a data collection form or case reporting form in REDCap
• perform data entry online using the web-based tool and offline using the REDCap mobile app
• set up surveys and perform data quality checks in REDCap
• understand how to use REDCap for different study designs (e.g. cross-sectional, longitudinal etc.)
• design, use, and maintain a custom-made redcap database for research studies
• manage basic data types in R calculations
• conduct basic computation with matrices and data frames in R
• identify and tidy messy data to prepare for analysis using R
• understand the importance of data visualization as a communication strategy
• understand the principles and key elements that make up an effective infographic
• establish a foundation for building infographics using Microsoft PowerPoint

 
課程要求
Students should read and review the reading material before and after the lecture. The slides of each lecture will be available on the course for students to download.  
預期每週課後學習時數
 
Office Hours
 
指定閱讀
 
參考書目
1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. (2009) Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support, J Biomed Inform. (http://www.sciencedirect.com/science/article/pii/S1532046408001226 )
2. Tippmann, S. (2014). Programming tools: Adventures with R. Nature, 517(7532), pp.109-110. (http://www.nature.com/polopoly_fs/1.16609!/menu/main/topColumns/topLeftColumn/pdf/517109a.pdf)
3. Venables, W.N. (2018). An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics (https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf)
4. Wickham, H. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (http://r4ds.had.co.nz/index.html)
 
評量方式
(僅供參考)
   
課程進度
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日期
單元主題
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