The New Computational and Data Sciences Undergraduate Program at George Mason University

  • Kirk Borne
  • John Wallin
  • Robert Weigel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5545)


We describe the new undergraduate science degree program in Computational and Data Sciences (CDS) at George Mason University (Mason), which began offering courses for both major (B.S.) and minor degrees in Spring 2008. The overarching theme and goal of the program are to train the next-generation scientists in the tools and techniques of cyber-enabled science (e-Science) to prepare them to confront the emerging petascale challenges of data-intensive science. The Mason CDS program has a significantly stronger focus on data-oriented approaches to science than do most computational science and engineering programs. The program has been designed specifically to focus both on simulation (Computational Science) and on data-intensive applications (Data Science). New courses include Introduction to Computational & Data Sciences, Scientific Data and Databases, Scientific Data & Information Visualization, Scientific Data Mining, and Scientific Modeling & Simulation. This is an interdisciplinary science program, drawing examples, classroom materials, and student activities from a broad range of physical and biological sciences. We will describe some of the motivations and early results from the program. More information is available at


Data Mining Data Science Digital Library Virtual Observatory Large Synoptic Survey Telescope 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Mahootian, F., Eastman, T.: Complementary Frameworks of Scientific Inquiry. World Futures journal (2009) (in press)Google Scholar
  2. 2.
    Bell, G., Gray, J., Szalay, A.: (2005)Google Scholar
  3. 3.
    Gray, J., Szalay, A.: Microsoft technical report MSR-TR-2004-110 (2004)Google Scholar
  4. 4.
    Becla, J., et al.: (2006)Google Scholar
  5. 5.
    Szalay, A.S., Gray, J., VandenBerg, J.: (2002)Google Scholar
  6. 6.
    Gray, J., et al.: (2002)Google Scholar
  7. 7.
    Borne, K.D.: Data-Driven Discovery through e-Science Technologies. In: 2nd IEEE Conference on Space Mission Challenges for Information Technology (2006)Google Scholar
  8. 8.
    Dunham, M.: Data Mining Introductory and Advanced Topics. Prentice-Hall, New Jersey (2002)Google Scholar
  9. 9.
    Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  10. 10.
    Gray, J., et al.: Scientific Data Management in the Coming Decade, (2005)Google Scholar
  11. 11.
    Butler, D.: Agencies Join Forces to Share Data. Nature 446, 354 (2007)CrossRefGoogle Scholar
  12. 12.
    Smith, F.: Data Science as an Academic Discipline. Data Science Journal 5, 163 (2006)CrossRefGoogle Scholar
  13. 13.
    Cleveland, W.S.: Data Science: an Action Plan for Expanding the Technical Areas of the Field of Statistics. International Statistics Review 69, 21 (2007)CrossRefzbMATHGoogle Scholar
  14. 14.
    NSF/JISC Repositories Workshop (2007),
  15. 15.
    Iwata, S.: Scientific “Agenda” of Data Science. Data Science Journal 7, 54 (2008)CrossRefGoogle Scholar
  16. 16.
    Baker, D.N.: Informatics and the 2007-2008 Electronic Geophysical Year. EOS 89, 485 (2008)CrossRefGoogle Scholar
  17. 17.
    Bits of Power: Issues in Global Access to Scientific Data,
  18. 18.
    Knowledge Lost in Information: Report of the NSF Workshop on Research Directions for Digital Libraries,
  19. 19.
    Report of the NSF Blue-Ribbon Advisory Panel on Cyberinfrastructure,
  20. 20.
    Cyberinfrastructure Vision for 21st Century Discovery,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kirk Borne
    • 1
  • John Wallin
    • 1
  • Robert Weigel
    • 1
  1. 1.Computational and Data SciencesGeorge Mason UniversityFairfaxUSA

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