TY - JOUR
T1 - Teaching Computational Social Science for All
AU - Kim, Jae Yeon
AU - Ng, Yee Man Margaret
N1 - Publisher Copyright:
Copyright © The Author(s), 2022. Published by Cambridge University Press.
PY - 2022/7/15
Y1 - 2022/7/15
N2 - Computational methods have become an integral part of political science research. However, helping students to acquire these new skills is challenging because programming proficiency is necessary, and most political science students have little coding experience. This article presents pedagogical strategies to make transitioning from Excel, SPSS, or Stata to R or Python for data analytics less challenging and more exciting. First, it discusses two approaches for making computational methods accessible: showing the big picture and walking through the workflow. Second, a step-by-step guide for a typical course is provided using three examples: learning programming fundamentals, wrangling messy data, and communicating data analysis.
AB - Computational methods have become an integral part of political science research. However, helping students to acquire these new skills is challenging because programming proficiency is necessary, and most political science students have little coding experience. This article presents pedagogical strategies to make transitioning from Excel, SPSS, or Stata to R or Python for data analytics less challenging and more exciting. First, it discusses two approaches for making computational methods accessible: showing the big picture and walking through the workflow. Second, a step-by-step guide for a typical course is provided using three examples: learning programming fundamentals, wrangling messy data, and communicating data analysis.
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U2 - 10.1017/S1049096521001815
DO - 10.1017/S1049096521001815
M3 - Article
AN - SCOPUS:85124996830
SN - 1049-0965
VL - 55
SP - 605
EP - 609
JO - PS - Political Science and Politics
JF - PS - Political Science and Politics
IS - 3
ER -