Equip public managers with data analytics skills: a proposal for the new generation of MPA/MPP programs with data science track

Abstract

PurposeThis research uses a multifaceted approach to develop an MPA/MPP curriculum to support a data science track within the existing MPA/MPP programs by identifying the core and elective areas needed.Design/methodology/approachThe approach includes (1) identifying a suitable structure for MPA/MPP programs which can allow the program to develop its capacity to train students with the data science and general public administration skills to solve public policy problems and leave explicit space for local experimentation and modification; (2) defining bridging modules and required modules for the MPA/MPP programs; and (3) developing of data science track thought to make suggestions for the inclusion of suitable data science modules into the data science track and benchmarking the data science modules suggested with the best practices developed by other professional bodies. The authors review 46 NASPAA-accredited MPA/MPP programs from 40 (or 22.7%) schools to identify the suitable required modules and some potential data science and analytics courses that MPA/MPP programs currently provide as electives.FindingsThe proposal includes a three-course (six–nine credits, not counted in the program but as prerequisites) bridging module, a nine-course (27 credits) required module and a five-course (15 credits) data science track/concentration.Originality/valueThis work can provide a starting point for the public administration education community to develop graduate programs focusing on data science to cater to the needs of both public managers and society at large.

Editorial: Special selection on current bibliometrics and reviews

“…Results indicate that the research growth rate is expected to gradually increase and reveal countries and topics of research focus. Readers may be interested in reading recent articles on curriculum development for the Master of Public Administration (Ho et al, 2023a), urban governance (Zhang et al, 2023), IoT as a societal transformer (Chohan et al, 2023), open government data (Dai et al, 2023;Wu et al, 2022;Wang et al, 2023c) and knowledge management in governments (Lam et al, 2023). Khan et al (2024a) performed a systematic review and found a significant impact of information creditability on the information adoption behavior of social media users.…”

Editorial: Special selection on contemporary bibliometric analytics

“…More institutions have moved to online learning mode in the past few years due to COVID-19 (Kabigting et al ., 2023; Li et al ., 2023; Tse et al ., 2022; Ye and Ho, 2022; Yi and Chiu, 2023; Yu et al ., 2023) and changed information habits of youths (Ding et al ., 2021b; Dong et al ., 2021; Yu et al ., 2022) and curricula (Ho et al ., 2023; Yew et al ., 2022; Li and Chiu, 2022; Ng et al ., 2022). Thus, a focus is on the dropout rates of massive open online courses (MOOCs).…”

Editorial: Advances in information and knowledge management

“…Their results revealed that e-service quality positively predicts e-trust in the digital economy, e-trust predicts e-loyalty and e-trust mediates the relationship between e-service quality and e-loyalty links. Readers may also be interested in reading further into e-trust issues on social media (Nabi et al ., 2023; Wang et al ., 2021; Wang and Tse, 2022; Ye and Ho, 2023; Yuan and Bi, 2023; Zhang et al ., 2023a, b, c), contact-tracing apps (Ho et al ., 2023a, b) and chatbots (Hsiao and Chen, 2022). Besides, readers may be interested in issues related to library promotion and marketing (Chan et al ., 2020; Cheng et al ., 2020; Fong et al ., 2020; Kwan et al ., 2023; Lam et al ., 2019, 2023; Liu et al ., 2023; Sahli et al ., 2023; Wójcik, 2022; Wu and Yang, 2022).…”

“…They revealed that skill requirements and tool usage vary significantly with educational background, and data scientists surveyed spend more time analyzing data than operationalizing insights. Readers may be interested to read more on educational issues related to data science (Ho et al ., 2023b).…”