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== 한국 ==
충북대학교 :: 비즈니스 데이터 융합학과 (http://www.bloter.net/archives/109660)
국민대학교 :: 빅데이터경영 MBA 과정
비즈니스 애널리틱스
서울대학교 :: 빅데이터 센터 (http://bigdata.snu.ac.kr/bx/), 컴퓨터공학부, 산업공학과, 통계학과, 전기정보학부
1. 빅데이터 인프라 기술,
1. 데이터 과학 및 분석기술
1. 법, 정책
1. 보건의료
1. 생명, 환경
1. 미래산업경제
1. 사회복지
1. 방송, 문화, 스포츠
1. 인력양성
경희대학교 :: 소셜네트워크분석과학 학과
한양대학교 ::
== Related program ==
[http://www.forbes.com/sites/danwoods/2011/11/27/linkedins-monica-rogati-on-what-is-a-data-scientist/print/ LinkedIn's Monica Rogati On "What Is A Data Scientist?"] Forbes
[http://www.citoresearch.com/data-science/growing-your-own-data-scientists Growing Your Own Data Scientists]

[http://iacs.seas.harvard.edu/ Institute for Applied Computational Science, Harvard University]

[http://idse.columbia.edu/ Institute for Data Sciences, Columbia University]
[https://amplab.cs.berkeley.edu/ AMPLab University of California Berkeley]


[http://www.heinz.cmu.edu/school-of-information-systems-and-management/information-systems-management-mism/index.aspx Master of Information Systems Management (MISM), Carnegie Mellon]
[http://heinz.cmu.edu/school-of-information-systems-and-management/information-systems-management-mism/business-intelligence-data-analytics/index.aspx Business Intelligence and Data Analytics (BIDA), Carnegie Mellon]
[http://www.analytics.northwestern.edu/ Master of Science in Analytics, Northwestern University]
[http://analytics.ncsu.edu/ Master of Science in Analytics, North Carolina State University]
[http://scpd.stanford.edu/ppc/massive-datasets-courses.jsp Data Mining and Applications Graduate Certificate, Stanford]

[http://www.dbguide.net/bigacademy.db?cmd=intro22 빅데이터 아카데미, 한국데이터베이스 진흥원], http://www.dbguide.net/bigacademy.db

[http://cmsdv.yonsei.ac.kr/gsi/sub03/sub0311/sub03_11.asp 빅데이터 석사과정, 연세대학교]
[http://www.hanyang.ac.kr/user/indexSub.action?codyMenuSeq=1714&siteId=hanyangkr2&menuType=T&uId=3&sortChar=ABC&menuFrame=&linkUrl=03_02_03.html&mainFrame=right&dum=dum&command=curriculum_list&viewHakgwajojikCd=Y3YEAD&language=kor&viewStructureSeq=333 정보사회학과, 한양대학교]
[http://www.ssu.ac.kr/web/inso/intro_c 정보사회학과, 숭실대학교]
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[http://www.moore.org/newsroom/press-releases/2013/11/12/%20bold_new_partnership_launches_to_harness_potential_of_data_scientists_and_big_data Bold new partnership launches to harness potential of data scientists and big data]
New York University, the University of California, Berkeley and the University of Washington launch a 5-year, $37.8 million cross-institutional effort with support from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation.
Washington, D.C. A new multi-million dollar collaboration will enable university researchers to harness the full potential of the data-rich world that characterizes all fields of science and discovery. This ambitious partnership, which includes New York University, the University of California, Berkeley and the University of Washington, will spur collaborations within and across the three campuses and other partners pursuing similar data-intensive science goals.
The new 5-year, $37.8 million initiative, with support from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation, was announced at a meeting sponsored by the White House Office of Science and Technology Policy (OSTP) focused on developing innovative partnerships to advance technologies that support advanced data management and data analytic techniques.
At a time when the natural, mathematical, computational and social sciences are all producing data with relentlessly increasing volume, variety and velocity, capturing the full potential of a progressively data-rich world has become a daunting hurdle for both data scientists and those who use data science to advance their research.
While data science is already contributing to scientific discovery, substantial systemic challenges need to be overcome to maximize its impact on academic research.
To overcome these challenges, this effort seeks to achieve three core goals:
1. Develop meaningful and sustained interactions and collaborations between researchers with backgrounds in specific subjects (such as astrophysics, genetics, economics), and in the methodology fields (such as computer science, statistics and applied mathematics), with the specific aim of recognizing what it takes to move each of the sciences forward;
1. Establish career paths that are long-term and sustainable, using alternative metrics and reward structures to retain a new generation of scientists whose research focuses on the multi-disciplinary analysis of massive, noisy, and complex scientific data and the development of the tools and techniques that enable this analysis; and

1. Build on current academic and industrial efforts to work towards an ecosystem of analytical tools and research practices that is sustainable, reusable, extensible, learnable, easy to translate across research areas and enables researchers to spend more time focusing on their science.
== Academic Programs ==

"Dramatic expansion in the scale of data collection, analysis and dissemination could revolutionize the speed and volume of discovery,” said Chris Mentzel, Moore’s Data-Driven Discovery program officer. “However, success ultimately depends on the individuals and teams that combine domain expertise with computational, statistical and mathematical skills ? what we are calling 'data science.'"
협동연구 - 통계학, 사회학의 협동연구 예: [http://www.moore.org/newsroom/press-releases/2013/11/12/%20bold_new_partnership_launches_to_harness_potential_of_data_scientists_and_big_data Bold new partnership launches to harness potential of data scientists and big data] New York University, the University of California, Berkeley and the University of Washington launch a 5-year, $37.8 million cross-institutional effort with support from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation.

"It’s been hard to establish these essential roles as durable and attractive career paths in academic research,” explained Josh Greenberg, who directs the Sloan Foundation’s Digital Information Technology program. “This joint project will work to create examples at the three universities that demonstrate how an institution-wide commitment to data scientists can deliver dramatic gains in scientific productivity."

The initiative will tap leading researchers at their respective institutions ? and some of the best minds in science and academia. Faculty leads include:
[http://blogs.oii.ox.ac.uk/policy/five-recommendations-for-maximising-the-relevance-of-social-science-research-for-public-policy-making-in-the-big-data-era/ Five recommendations for maximising the relevance of social science research for public policy-making in the big data era] The Policy and Internet Blog

Yann LeCun, Silver Professor of Computer Science and Neural Science at New York University's Courant Institute of Mathematical Sciences and founding director of New York University's Center for Data Science; Saul Perlmutter, professor of physics at the University of California, Berkeley, astrophysicist at Lawrence Berkeley National Laboratory, and Nobel laureate; and Ed Lazowska, Bill & Melinda Gates Chair in Computer Science & Engineering at the University of Washington and director of the University of Washington’s eScience Institute.
[http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-masters-degrees-20-top-programs/d/d-id/1108042?page_number=1 Big Data Analytics Master's Degrees: 20 Top Programs]

The three leaders believe universities are uniquely positioned to empower researchers to harness the deluge of valuable, heterogeneous, and noisy data continuing to come their way ? and help navigate the flood of software analysis tools and approaches that are often incompatible, hard to learn or poorly written by brilliant scientists trying to get their job done.
* [http://www.heinz.cmu.edu/school-of-information-systems-and-management/information-systems-management-mism/index.aspx Master of Information Systems Management (MISM), Carnegie Mellon]
* [http://heinz.cmu.edu/school-of-information-systems-and-management/information-systems-management-mism/business-intelligence-data-analytics/index.aspx Business Intelligence and Data Analytics (BIDA), Carnegie Mellon]
* [http://idse.columbia.edu/ Institute for Data Sciences, Columbia University]
* {{| DUAL MS IN JOURNALISM AND COMPUTER SCIENCE
Admitted students will enroll for a total of five semesters — approximately three in The Fu Foundation School of Engineering and Applied Science and two in the Journalism School. In addition to taking classes already offered at the Journalism and Engineering schools, students will attend a seminar and workshop designed specifically for the dual degree program. The seminar will teach students about the impact of digital techniques on journalism; the emerging role of citizens in the news process; the influence of social media; and the changing business models that will support newsgathering. In the workshop, students will use a hands-on approach to delve deeply into information design, focusing on how to build a site, section or application from concept to development, ensuring the editorial goals are kept uppermost in mind. For more information, please visit the program website.
The AP-Google Journalism Technology Scholarship has been announced. For more information, please visit their website.
|}}
* http://www.journalism.columbia.edu/page/276-dual-degree-journalism-computer-science/279
* http://www.journalism.columbia.edu/system/documents/372/original/2013_JNCOM_FAQs.pdf
* http://www.journalism.columbia.edu/system/documents/693/original/jschool_compsci_singles.pdf
{{{
CORE REQUIREMENT
COMS W4111 Introduction to Databases
COMS W4115 Programming Language and Translators
COMS W4156 Advanced Software Engineering
COMS W4170 User Interface Design
CSOR W4231 Analysis of Algorithms
COMS W4701 Artificial Intelligence

“As someone whose research depends on the fluent use of data,” said Saul Perlmutter, lead faculty member at the University of California, Berkeley, “I'm excited that we now have an opportunity to identify the typical data-science barriers, little and big, that slow our progress, and to see which could be mitigated ? or, occasionally, just plain solved!”
REQUIRED TRACK COURSE (3)
COMS W4112 Database Systems Implementation
CSEE W4119 Computer Networks
COMS W4160 Computer Graphics
COMS W4162 Advanced Computer Graphics
COMS W4172 3D User Interfaces and Augmented Reality
COMS W4180 Network Security
COMS W4705 Natural Language Processing
COMS W4706 Spoken Language Processing
COMS W4731 Computer Vision
COMS W4771 Machine Learning
COMS W4772 Advanced Machine Learning
COMS W4999 Computing and the Humanities
COMS W4995 Topics in CS (if focus is appropriate, needs approval from track advisor)

“We must build on our existing efforts that leverage existing industry tools, generate new working tools and practices and support the multi-disciplinary experts who develop new approaches and tools needed to fill gaps,” said Ed Lazowska, faculty lead at the University of Washington. “Working together, we believe we're going to shift the culture at our universities ? and help accelerate broader uptake ? for supporting data-intensive discovery.”
ELECTIVE TRACK COURSES (2)
COMS E6113 Topics in Database Systems
COMS E6125 Web-Enhanced Information Management
COMS E6175 Interaction Design
COMS E6176 User Interfaces for Mobile and Wearable Computing
COMS E6733 3D Photography
COMS E6734 Computational Photography
COMS E6735 Visual Databases
COMS E6901 Projects in CS
ELEN E6850 Visual Information Systems
COMS E6998 Topics in CS with appropriate focus
}}}

“With the onslaught of data, much of the knowledge in the world is going to be extracted by machines,” said Yann LeCun, faculty lead at New York University. “Universities must find new ways to advance data-science methodologies while facilitating the use of new methods and tools by researchers from every field. Universities also have an opportunity to train new generations of researchers in data-driven science.”
* [http://bdss.psu.edu/ Big Data Social Science] PSU
{{{Core seminars for all IGERT Students. (Beginning 2014)
Approaches and Issues in Big Social Data
Approaches and Issues in Social Data Analytics

Each of the three universities will contribute additional resources to the investment made by the Moore and Sloan foundations, including new faculty positions, physical space on campus and research support.
Analytics distribution, one course in a core approach to analytics:
statistical / machine learning or visual analytics.
Data Mining (STAT/IST)
Machine Learning (CSE)
Visual Analytics: Leveraging Geosocial Data (GEOG)

Each of the partner universities distinguished itself in recent years by pioneering new approaches to discovery in fields as diverse as astronomy, biology, oceanography, and sociology through deep collaborations between researchers in these fields and researchers in data science methodology fields such as computer science, statistics and applied mathematics.
Ethics and Scientific Responsibility distribution requirement
Privacy in Statistical Databases (STAT/CSE)
Data Privacy, Learning, and Games (CSE)
Big Social Data and the Law (PLSC/CLJ)
The Information Environment (IST)

This new partnership ? a coordinated, distributed experiment involving researchers at these leading universities ? hopes to establish models that will dramatically accelerate this data science revolution by addressing several specific challenges.
Social Data Analytics electives with substantial non-social science content (Social Science students)
Network Science (PHYS)
Vision-Based Tracking (CSE)
Computational Regularity on Interdisciplinary, Large Data Sets (CSE)
Pattern Recognition (CSE)
Information Retrieval and Organization (IST)
Web Analytics (IST)
Spatial Analysis (GEOG)
Most graduate courses in Statistics

Cross-university teams will organize their efforts around six primary areas: strengthening an ecosystem of tools and software environments, establishing academic careers for data scientists, championing education and training in data science at all levels, promoting and facilitating efforts that are accessible and reproducible, creating physical and intellectual hubs for data science activities, and identifying the scientists’ data-science bottlenecks and needs through directed ethnography.
Social Data Analytics electives with substantial social science content (Non-Social Science students)
Modeling Interdependent Data (CAS)
Political Event Data and Forecasting (PLSC)
Social Network Analysis (SOC)
Causal Inference (PLSC)
Democratic Representation: Big Data Approaches (PLSC)
Multilevel Modeling (SOC)
Spatial Demography (SOC)
Intensive Longitudinal Data (HDFS)
Geospatial Science in Anthropology (ANTH)

This partnership will connect with others, practice open science and share lessons along the way.
}}}

The Gordon and Betty Moore Foundation believes in bold ideas that create enduring impact in the areas of science, environmental conservation and patient care. Intel co-founder Gordon and his wife Betty established the foundation to create positive change around the world and at home in the San Francisco Bay Area. Science looks for opportunities to transform ? or even create ? entire fields by investing in early-stage research, emerging fields and top research scientists. Our environmental conservation efforts promote sustainability, protect critical ecological systems and align conservation needs with human development. Patient care focuses on eliminating preventable harms and unnecessary healthcare costs through meaningful engagement of patients and their families in a supportive, redesigned healthcare system. For more information, please visit www.moore.org or follow @MooreScientific.

The Alfred P. Sloan Foundation is a philanthropic, not-for-profit grantmaking institution that supports original research and education in science, technology, engineering, mathematics, and economic performance. Funds for this project were provided through the Foundation's Digital Information Technology program, which leverages developments in information technology to increase the effectiveness of computational research and scholarly communication. For more information, please visit www.sloan.org.
== Interview and Insights ==
[http://blogs.lse.ac.uk/impactofsocialsciences/2012/09/19/five-minutes-with-prabhakar-raghavan/ Five minutes with Prabhakar Raghavan: Big data and social science at Google] :: Part of PPG's Impact of Social Sciences project focuses on how academic research in the social sciences influences decision-makers in business, government and civil society. We will cover a series of salient viewpoints emerging from this interview programme on the blog over the next three months. To launch the series Rebecca Mann talked to Prabhakar Raghavan, who is Vice President of Strategic Technologies at Google, and Consulting Professor of Computer Science at Stanford. He explains the role that social scientists are already playing in the development of the tech sector in Silicon Valley, and discusses the opportunities for impact and some remaining obstacles to collaboration.

New York University, founded in 1831, is one of the world’s foremost research universities and a member of the selective Association of American Universities. The first Global Network University, it has degree-granting university campuses in New York, Abu Dhabi, and Shanghai; 11 other global academic sites; and sends more students to study abroad than any other U.S. college or university. Through its 18 schools and colleges, NYU conducts research and provides education in the arts and sciences, law, medicine, business, dentistry, education, nursing, the cinematic and performing arts, music and studio arts, public administration, social work, engineering, and continuing and professional studies, among other areas. For more information, please visit www.nyu.edu or follow @nyuniversity.
[http://blogs.lse.ac.uk/impactofsocialsciences/2011/12/09/five-minutes-with-andrew-miller-mp-%E2%80%9Cit%E2%80%99s-important-that-people-handle-information-in-an-intelligent-way-and-social-science-has-a-huge-role-in-this%E2%80%9D/ Five minutes with Andrew Miller MP: “It’s important that people handle information in an intelligent way, and social science has a huge role in this.”] . . . . It’s important that people handle information in an intelligent way, and social science has a huge role in this. . . .

The University of California, Berkeley is the world's premier public university with a mission to excel in teaching, research and public service. This longstanding mission has led to the university's distinguished record of Nobel-level scholarship, constant innovation, a concern for the betterment of our world and consistently high rankings of its schools and departments. The campus offers superior, high value education for extraordinarily talented students from all walks of life; operational excellence and a commitment to the competitiveness and prosperity of California and the nation. For more information, please visit www.berkeley.edu.
[http://www.oii.ox.ac.uk/research/projects/?id=98 Accessing and Using Big Data to Advance Social Science Knowledge] . . . . A project initiated at the University of Oxford with a title, "Accessing and Using Big Data to Advance '''Social Science Knowledge'''"

Founded in 1861, the University of Washington is one of the oldest public institutions of higher education on the West Coast and is one of the world’s preeminent research-intensive universities, with more than 100 members of the National Academies, elite programs in many fields, and annual standing since 1974 among the top five universities in receipt of federal research funding. For more information, please visit www.washington.edu.
[http://www.kukey.com/news/articleView.html?idxno=19545 대학의 특화된 교육과정 필요해 - 빅데이터] 고대신문 . . . . 삼성전자 MSC 플랫폼개발팀 박재현 상무도 "이공계, 인문계, 상경계 등 분야를 가리지 않고 빅데이터 인재를 모으기 위해 주력할 것"이라며 "현재 고려대 전산과와 빅데이터 교육을 추진 중이다"라고 전했다. . . . "빅데이터는 여러 분야에서 화두가 되는 융합 도구이기 때문에 문·이과를 합한 빅데이터 대학원 연계전공을 개설하면 좋겠다"

[[ColEnd]]


한국

충북대학교 :: 비즈니스 데이터 융합학과 (http://www.bloter.net/archives/109660)
국민대학교 :: 빅데이터경영 MBA 과정
비즈니스 애널리틱스
서울대학교 :: 빅데이터 센터 (http://bigdata.snu.ac.kr/bx/), 컴퓨터공학부, 산업공학과, 통계학과, 전기정보학부
  1. 빅데이터 인프라 기술,
  2. 데이터 과학 및 분석기술
  3. 법, 정책
  4. 보건의료
  5. 생명, 환경
  6. 미래산업경제
  7. 사회복지
  8. 방송, 문화, 스포츠
  9. 인력양성


경희대학교 :: 소셜네트워크분석과학 학과
한양대학교 ::

Related program

Academic Programs


협동연구 - 통계학, 사회학의 협동연구 예: [http]Bold new partnership launches to harness potential of data scientists and big data New York University, the University of California, Berkeley and the University of Washington launch a 5-year, $37.8 million cross-institutional effort with support from the Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation. 





CORE REQUIREMENT
COMS W4111	Introduction to Databases
COMS W4115	Programming Language and Translators
COMS W4156	Advanced Software Engineering
COMS W4170	User Interface Design
CSOR W4231	Analysis of Algorithms
COMS W4701	Artificial Intelligence

REQUIRED TRACK COURSE (3)
COMS W4112	Database Systems Implementation
CSEE W4119	Computer Networks
COMS W4160	Computer Graphics
COMS W4162	Advanced Computer Graphics
COMS W4172	3D User Interfaces and Augmented Reality
COMS W4180	Network Security
COMS W4705	Natural Language Processing
COMS W4706	Spoken Language Processing
COMS W4731	Computer Vision
COMS W4771	Machine Learning
COMS W4772	Advanced Machine Learning
COMS W4999	Computing and the Humanities
COMS W4995	Topics in CS (if focus is appropriate, needs approval from track advisor)

ELECTIVE TRACK COURSES (2)
COMS E6113	Topics in Database Systems
COMS E6125	Web-Enhanced Information Management
COMS E6175	Interaction Design
COMS E6176	User Interfaces for Mobile and Wearable Computing
COMS E6733	3D Photography
COMS E6734	Computational Photography
COMS E6735	Visual Databases
COMS E6901	Projects in CS
ELEN E6850	Visual Information Systems
COMS E6998	Topics in CS with appropriate focus



Core seminars for all IGERT Students. (Beginning 2014)
Approaches and Issues in Big Social Data
Approaches and Issues in Social Data Analytics

Analytics distribution, one course in a core approach to analytics:
statistical / machine learning or visual analytics.
Data Mining (STAT/IST)
Machine Learning (CSE)
Visual Analytics: Leveraging Geosocial Data (GEOG)

Ethics and Scientific Responsibility distribution requirement
Privacy in Statistical Databases (STAT/CSE)
Data Privacy, Learning, and Games (CSE)
Big Social Data and the Law (PLSC/CLJ)
The Information Environment (IST)

Social Data Analytics electives with substantial non-social science content (Social Science students)
Network Science (PHYS)
Vision-Based Tracking (CSE)
Computational Regularity on Interdisciplinary, Large Data Sets (CSE)
Pattern Recognition (CSE)
Information Retrieval and Organization (IST)
Web Analytics (IST)
Spatial Analysis (GEOG)
Most graduate courses in Statistics

Social Data Analytics electives with substantial social science content (Non-Social Science students)
Modeling Interdependent Data (CAS)
Political Event Data and Forecasting (PLSC)
Social Network Analysis (SOC)
Causal Inference (PLSC)
Democratic Representation: Big Data Approaches (PLSC)
Multilevel Modeling (SOC)
Spatial Demography (SOC)
Intensive Longitudinal Data (HDFS)
Geospatial Science in Anthropology (ANTH)


Interview and Insights

[http]Five minutes with Prabhakar Raghavan: Big data and social science at Google :: Part of PPG's Impact of Social Sciences project focuses on how academic research in the social sciences influences decision-makers in business, government and civil society. We will cover a series of salient viewpoints emerging from this interview programme on the blog over the next three months. To launch the series Rebecca Mann talked to Prabhakar Raghavan, who is Vice President of Strategic Technologies at Google, and Consulting Professor of Computer Science at Stanford. He explains the role that social scientists are already playing in the development of the tech sector in Silicon Valley, and discusses the opportunities for impact and some remaining obstacles to collaboration.

[http]Five minutes with Andrew Miller MP: “It’s important that people handle information in an intelligent way, and social science has a huge role in this.” . . . . It’s important that people handle information in an intelligent way, and social science has a huge role in this. . . .

[http]Accessing and Using Big Data to Advance Social Science Knowledge . . . . A project initiated at the University of Oxford with a title, "Accessing and Using Big Data to Advance Social Science Knowledge"

[http]대학의 특화된 교육과정 필요해 - 빅데이터 고대신문 . . . . 삼성전자 MSC 플랫폼개발팀 박재현 상무도 "이공계, 인문계, 상경계 등 분야를 가리지 않고 빅데이터 인재를 모으기 위해 주력할 것"이라며 "현재 고려대 전산과와 빅데이터 교육을 추진 중이다"라고 전했다. . . . "빅데이터는 여러 분야에서 화두가 되는 융합 도구이기 때문에 문·이과를 합한 빅데이터 대학원 연계전공을 개설하면 좋겠다"

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