Data Science Curriculums Around the World: An Empirical Study

doi:10.11925/infotech.2096-3467.2017.06.02

Data Analysis and Knowledge Discovery

2017, Vol. 1

Issue (6): 12-21 DOI: 10.11925/infotech.2096-3467.2017.06.02

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Data Science Curriculums Around the World: An Empirical Study

Chao Lemen^1,²(

), Yang Canjun², Wang Shengjie², Zhao Junpeng², Xu Mengtian²

¹Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Beijing 100872, China
²School of Information Resource Management, Renmin University of China, Beijing 100872, China

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Abstract

[Objective] This paper identifies the common features of existing Data Science curriculums around the world. It also addresses the main challenges facing these courses as well as possible solutions. [Methods] We conducted an empirical study with the help of text analysis techniques to examine the data science curriculums from China and abroad. [Results] We found common features of the retrieved curriculums and the differences between them and other related courses. [Limitations] Our study focused on the curriculum issues, therefore, more research is needed to discuss data science as a discipline. [Conclusions] This paper addresses the top ten key challenges facing data science curriculum and then proposes some solutions.

Key words： Data Science Big Data Curriculum Design

Received: 12 June 2017 Published: 25 August 2017

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	Lemen Chao
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Cite this article:

Chao Lemen,Yang Canjun,Wang Shengjie,Zhao Junpeng,Xu Mengtian. Data Science Curriculums Around the World: An Empirical Study. Data Analysis and Knowledge Discovery, 2017, 1(6): 12-21.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.06.02 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I6/12

[1]	Baumer B.A Data Science Course for Undergraduates: Thinking with Data[J]. The American Statistician, 2015, 69(4): 334-342. doi: 10.1080/00031305.2015.1081105
[2]	Paisley J. Machine Learning for Data Science [OL]. [2016- 09-25]. .
[3]	Grimson E.Introduction to Computational Thinking and Data Science [OL]. [2016-08-25]..
[4]	D’Alessandro B. Intro to Data Science [OL]. [2016-08-25]. .
[5]	Pozdnukhov A. Data Science for Smart Cities [OL]. [2016- 08-25]. .
[6]	Mata-Toledo R A. A Practical Approach to Data Science [OL].[2016-08-25]. .
[7]	Anscombe F J.Graphs in Statistical Analysis[J]. The American Statistician, 1973, 27(1): 17-21.
[8]	Schutt R, O’Neil C. Doing Data Science: Straight Talk from the Frontline[M]. O’Reilly Media, Inc., 2013.
[9]	Zumel N, Mount J, Porzak J.Practical Data Science with R[M]. Manning Publications, 2014.
[10]	Holtz D.Introduction to Data Science [OL]. [2016-08-26]..
[11]	Microsoft. NUS and Microsoft Collaborate on Data Science Education and Research [OL].[2016-09-28]. .
[12]	Howe B, Franklin M J, Freire J, et al.Should We All be Teaching intro to Data Science Instead of Intro to Databases[C]//Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. ACM, 2014: 917-918.
[13]	朝乐门. 数据科学[M]. 北京: 清华大学出版社, 2016: 41-42.
[13]	(Chaolemen Borjigin.Data Science [M]. Beijing: Tsinghua University Press, 2016: 41-42.)
[14]	朝乐门. 数据科学理论与实践[M]. 北京: 清华大学出版社, 2017: 15.
[14]	(Chaolemen Borjigin.Data Science: Theory and Practice [M]. Beijing: Tsinghua University Press, 2017: 15.)

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