Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (6): 12-21    DOI: 10.11925/infotech.2096-3467.2017.06.02
Orginal Article Current Issue | Archive | Adv Search |
Data Science Curriculums Around the World: An Empirical Study
Chao Lemen1,2(), 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
Download: PDF (1280 KB)   HTML ( 7
Export: BibTeX | EndNote (RIS)      

[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 wordsData Science      Big Data      Curriculum Design     
Received: 12 June 2017      Published: 25 August 2017
ZTFLH:  TP393  

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.

URL:     OR

课程名称 年份 形式 学校 开课教师 选课要求
Data Science: Large-scale Advanced Data
2011 面授 佛罗里达大学 Daisy Zhe Wang 硕士
Data Science and Analytics Thought Leaders 2012 面授 加州大学伯克利分校 Ram Akella等 不限
Introduction to Data Science 2012 面授 哥伦比亚大学 Rachel Schutt 不限
Introduction to Data Science 2013 面授 谢菲尔德大学 Paul Clough 数据相关/硕士
Data Science(Coursea) 2014 网授 约翰·霍普金斯大学 Roger D. Peng等 不限
Executive Data Science(Coursea) 2014 网授 约翰·霍普金斯大学 Roger D. Peng等 不限
Data Science at Scale (Coursea) 2014 网授 华盛顿大学 Bill Howe 不限
Data Science 2014 面授 哈佛大学 Rafael Irizarry等 本科
Intro to Data Science 2014 面授 纽约大学 Brian D’Alessandro 不限
大数据科学与应用系列讲座(MOOC学院) 2015 网授 清华大学 李军 不限
Foundations of Data Science 2015 面授 加州大学伯克利分校 John DeNero 不限
Data Sciences Basic 2015 面授 美国东北大学 Akira Suzuki 不限
Fundamentals of Data Science 2015 面授 慕尼黑大学 Goeran Kauermann 统计与科学相关
A Practical Approach to Data Science 2016 面/网授 哈佛大学 Ramon Mata-Toledo 不限
Introduction to Computational Thinking and
Data Science (edx)
2016 网授 麻省理工学院(MIT) Eric Grimson等 不限
Process Mining: The Practice of
Data Science (Coursea)
2016 网授 埃因霍芬理工大学 Wil van der Aalst 硕士
Data Science 2016 面授 法国圣艾蒂安大学 Marc Sebban 不限
Fundamentals of Data Science 2017 面授 牛津大学 Julian Gallop 不限
数据科学 2017 面授 中国人民大学 朝乐门 不限
Data Science 不详 面授 伦敦大学 Aysha Chaudhary 数据相关/硕士
对比项目 数据工程师 数据科学家
工作重点 数据的管理 基于数据的管理/决策
基本素质 工程化/标准化/
领域差异性 领域共性较高,
A Practical Approach to Data Science
Intro to Data Science
Intro to Data Science
A Crash Course in Data Science
Introduction to Data Science
Fundamentals of Data Science
统计学 ×
机器学习 × × ×
数据可视化 × × ×
数据管理 × ×
数据计算 × ×
数据分析 ×
对比项目 专业数据科学家 专业中的数据科学家
成长过程 起点并非领域专家, 通过学习数据科学课程直接成长为数据
先已成为领域专家, 然后通过学习数据科学课程逐渐成为数据科学家
知识广度(数据科学) 较小(仅限于数据科学) 较大(不仅掌握领域知识, 而且还掌握数据科学)
知识深度(数据科学) 较深 较高
角色定位 指导、组织、管理、监督、评价专业中的数据科学家 配合与支持专业数据科学家
相关课程(举例) 华盛顿大学的《Intro to Data Science》; 哈佛大学的《A Practical
Approach to Data Science》; 中国人民大学的《数据科学》
哥伦比亚大学的《Introduction to Data Science》;
序号 设计原则 应该 不应该
1 最终目标 培养数据科学家 培养数据工程师/
2 主要特色 侧重数据科学的
3 首要任务 培育兴趣与自学能力 讲解数据科学的
4 基本前提 统筹数据科学课程链 脱离于相关课程
[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.)
[1] Chang Zhijun,Qian Li,Xie Jing,Wu Zhenxin,Zhang Hu,Yu Qianqian,Wang Ying,Wang Yongji. Big Data Platform for Sci-Tech Literature Based on Distributed Technology[J]. 数据分析与知识发现, 2021, 5(3): 69-77.
[2] Chen Shiji, Qiu Junping, Yu Bo. Topic Analysis of LIS Big Data Research with Overlay Mapping[J]. 数据分析与知识发现, 2021, 5(10): 51-59.
[3] Zhao Yuxiang,Lian Jingwen. Review of Cultural Heritage Crowdsourcing in the Domain of Digital Humanities[J]. 数据分析与知识发现, 2021, 5(1): 36-55.
[4] Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[5] Wang Jiandong,Yu Shiyang. Principles on Constructing National Economic Brain[J]. 数据分析与知识发现, 2020, 4(7): 2-17.
[6] Jiandong Wang. Monitoring and Forecasting Economic Performance with Big Data[J]. 数据分析与知识发现, 2020, 4(1): 12-26.
[7] Beibei Kong,Jing Xie,Li Qian,Zhijun Chang,Zhenxin Wu. Methodology and Tools to Enrich Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(7): 113-122.
[8] Xiaozhou Dong,Xinkang Chen. E-Coupon and Economic Performance of E-commerce[J]. 数据分析与知识发现, 2019, 3(6): 42-49.
[9] Quan Lu,Anqi Zhu,Jiyue Zhang,Jing Chen. Research on User Information Requirement in Chinese Network Health Community: Taking Tumor-forum Data of Qiuyi as an Example[J]. 数据分析与知识发现, 2019, 3(4): 22-32.
[10] Ying Wang,Li Qian,Jing Xie,Zhijun Chang,Beibei Kong. Building Knowledge Graph with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 15-26.
[11] Li Qian,Jing Xie,Zhijun Chang,Zhenxin Wu,Dongrong Zhang. Designing Smart Knowledge Services with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 4-14.
[12] Jiying Hu,Jing Xie,Li Qian,Changlei Fu. Constructing Big Data Platform for Sci-Tech Knowledge Discovery with Knowledge Graph[J]. 数据分析与知识发现, 2019, 3(1): 55-62.
[13] Jing Xie,Li Qian,Hongbo Shi,Beibei Kong,Jiying Hu. Designing Framework for Precise Service of Scholarly Big Data[J]. 数据分析与知识发现, 2019, 3(1): 63-71.
[14] Shen Zhihong,Yao Chang,Hou Yanfei,Wu Linhuan,Li Yuepeng. Big Linked Data Management: Challenges, Solutions and Practices[J]. 数据分析与知识发现, 2018, 2(1): 9-20.
[15] Yang Cao,Wenfei Fan,Tengfei Yuan. Is Big Data Analytics Beyond the Reach of Small Companies?[J]. 数据分析与知识发现, 2017, 1(9): 1-7.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938