Data science:the demand and development of talents

CCCHAN Keith; Tiantian HE

doi:10.11959/j.issn.2096-0271.2016058

您当前的位置：

首页 >

文章列表页 >

Data science:the demand and development of talents

forum | 更新时间：2024-06-03

- Data science:the demand and development of talents
- Big data research Vol. 2, Issue 5, Pages: 2016058(2016)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2016058
  CLC： TP3
- Online First：2016-09，
  
  Published：20 September 2016
- 稿件说明：
移动端阅览
CCCHAN Keith, Tiantian HE. Data science:the demand and development of talents[J]. Big data research, 2016, 2(5): 2016058.
DOI：

CCCHAN Keith, Tiantian HE. Data science:the demand and development of talents[J]. Big data research, 2016, 2(5): 2016058. DOI： 10.11959/j.issn.2096-0271.2016058.

摘要

信息科技业已进入大数据时代。作为能够从大数据中挖掘知识的人才，数据科学家（data scientist）受到各行各业的青睐。首先从美国和中国主要的在线人才招聘平台收集数据，通过对比分析得出数据科学家与传统的数据分析师（data analyst）在工作性质、工作能力要求以及薪资待遇等方面的差别。其次，考察和总结了世界范围内优秀大学数据科学人才培养的概况，并与工业界的实际要求进行对比。根据以上两者之间的差异，就当前大学数据科学人才的培养提出了建议和对策。

Abstract

Information technology has entered the era of big data.As talents who can discover the knowledge in big data

data scientists are tremendously demanded.The differences between data scientists and data analysts in the job nature

entry requirement and even remuneration were presented.Through a careful survey of the current job markets in the US and China.Then

it was revealed the gap between the kind of talents that were required for the jobs and the kind of graduates that the universities were training out.After a gap analysis

the views to the kind of data science programs which we believe may best develop the talents for the current and future job market were presented.

关键词

Keywords

references

ZHU Y Y , XIONG Y . Protection and utilization of data resources [M ] . Shanghai : Shanghai Scientific and Technical PublishersPress , 2008 : 133 - 137 .

HOWE D , COSTANZO M， FEY P， et al . The future of biocuration [J ] . Nature , 2008 , 455 ( 7209 ): 47 - 50 .

MARX V . The big challenges of big data [J ] . Nature , 2013 , 498 ( 7453 ): 255 - 260 .

China Academyof Informationand Communications Technology . Survey on the development of big data in China [R ] . 2015 .

FAN L , HONG X , CHAO H , et al . Challenge and countermeasure of governing government big data [J ] . Big Data Research , 2016 , 2 ( 3 ): 27 - 38 .

PAN Z , CHENG X , YUAN X . Developing trend forecasting of big data in 2016 from CCF TFBD:interpretation and proposals [J ] . Big Data Research , 2016 , 2 ( 1 ): 105 - 113 .

ZHU Y Y , XIONG Y . Training data scientists in the era of big data [J ] . Big Data Research , 2016 , 2 ( 3 ): 106 - 112 .

CHAN K C C , WONG A K C , CHIU D K Y . Learning sequential patterns for probabilistic inductive prediction [J ] . IEEE Transactions on Systems Man and Cybernetics , 1994 , 24 ( 10 ): 1532 - 1547 .

CHING J Y , WONG A K C , CHAN K C C . Class-dependent discretization for inductive learning from continuous and mixed-mode data [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 1995 , 17 ( 7 ): 641 - 651 .

AU W H , CHAN K C , WONG A K , et al . Attribute clustering for grouping,selection,and classification of gene expression data [J ] . IEEE/ACM Transactions on Computational Biology and Bioinformatics , 2005 , 2 ( 2 ): 83 - 101 .

HU A L , CHAN K C C . Utilizing both topological and attribute information for protein complex identification in ppi networks [J ] . IEEE/ACM Transactions on Computational Biology and Bioinformatics , 2013 , 10 ( 3 ): 780 - 792 .

LIU Z , ZHANG Y , CHANG E Y , et al . Plda+:parallel latent dirichlet allocation with data placement and pipeline processing [J ] . ACM Transactions on Intelligent Systems and Technology , 2011 , 2 ( 3 ): 389 - 396 .

LIU Z , HUANG W , ZHENG Y , et al . Automatic keyphrase extraction via topic decomposition [C ] // Conference on Empirical Methods in Natural Language Processing,October 9-11,2010,Massachusetts,USA .[S.l.:s.n ] , 2010 : 366 - 376 .

MANYIKA J , CHUI M , BROWN B , et al . Big data:the next frontier for innovation,competition,and productivity [J ] .,2011. McKinsey Global Institute , 2011 .

RANSBOTHAM S , KIRON D , PRENTICE P K . The Talent Dividend [J ] . MIT Sloan Management Review , 2015 , 56 ( 4 ):1.

Views

769

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Toward construction of “data science” course group and “introduction to data science” course

Discussions of the value expectations of big data

Foundation issues for big data research

Training data scientists in the era of big data

A scalable parallel sorting algorithm by regular sampling for big data

Related Author

Xiongpai QIN

Yueguo CHEN

Cuiping LI

Yunpeng CHAI

Jun XU

Jirong WEN

Xiaoyong DU

Chongjun WANG

Related Institution

School of Computer Science，Fudan University

Shanghai Key Lab of Data Science

School of Computer Science, Fudan University

School of Information Science and Engineering, Lanzhou University

Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection, Ministry of Culture and Tourism, Lanzhou University

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273427 Visits Today：333

⁰