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
   
   
   

• Master of Information Systems Management (MISM), Carnegie Mellon
  
• Business Intelligence and Data Analytics (BIDA), Carnegie Mellon
  
• 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

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

• 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

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)