skip to main content
10.1145/2808006.2808028acmconferencesArticle/Chapter ViewAbstractPublication PagesiteConference Proceedingsconference-collections
research-article

Pillars of Analytics Applied in MS Degree in Information Sciences and Technologies

Authors Info & Claims
Published:29 September 2015Publication History

ABSTRACT

The Master of Science (MS) program in Information Sciences and Technologies (IST) at Rochester Institute of Technology conducted a significant upgrade of its curriculum in 2013, aiming to better prepare its graduates for the new trends and challenges in the fast evolving IT computing industry. In particular, the upgraded MS program places a strong emphasis on data analytics, where all students in the program get an intensive training in data analytics foundation in our core courses. Students can then continue with advanced work in the Analytics Track to receive deeper theoretical knowledge in the field. In this paper, we report our experience of offering this analytics-centric curriculum over the past two years. We first formally define four pillars of analytics and trace the skills needed to support each pillar and the courses that provide those skills. We then describe the course experiences through a sampling of the projects completed by students in their course work. We also provide some student feedback on the course experience. We conclude with a discussion of the capstone experience and a sampling of capstone projects. We show the movement toward analytics in the capstone experiences, particularly since the program began in 2013. The positive course experience and the fast increasing number of capstone projects in the analytics area show strong evidence about the initial success of the analytics-centric curriculum.

References

  1. Baeza-Yates, R. and Ribeiro-Neto, B. 2011. Modern Information Retrieval: The Concepts and Technology behind Search (2nd Edition), ACM Press Books. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bishop, C. 2007. Pattern Recognition and Machine Learning (Information Science and Statistics), Springer (October 1, 2007) Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Goli, S. Need for Tech Pros with Analytics Skills Keeps Growing. http://insights.dice.com/2014/05/05/need-tech-pros-analytics-skills-keeps-growing/, retrieved 5/7/2015Google ScholarGoogle Scholar
  4. James, G., Witten, D., Hastie, T. and Tibshirani, R. 2014. An Introduction to Statistical Learning with Applications in R, Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Kang, J., Holden, E. and Yu, Q. 2014. Design of an Analytic Centric MS Degree in Information Sciences and Technologies. In Proceedings of the SIGITE Conference on Information Technology Education (Atlanta, Georgia, USA, October 16--18, 2014). SIGITE'14. ACM, New York, NY, 147--152. DOI= http://dx.doi.org/10.1145/2656450.2656460 Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Manning, C., Raghavan, P. and Schütze, H. 2008. Introduction to Information Retrieval, Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. MS in Information Sciences & Technologies (IST) at RIT Capstone Guide: http://www.ist.rit.edu/assets/pdf/IST%20MS%20Capstone%20Guide.pdf, retrieved 5/7/2015.Google ScholarGoogle Scholar
  8. Pratt, M. 10 Hottest IT Skills for 2015. http://www.computerworld.com/article/2844020/10-hottest-it-skills-for-2015.html, retrieved 5/7/2015.Google ScholarGoogle Scholar
  9. Rajaraman, A. and Ullman, J. 2011. Mining of Massive Datasets, Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Pillars of Analytics Applied in MS Degree in Information Sciences and Technologies

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SIGITE '15: Proceedings of the 16th Annual Conference on Information Technology Education
      September 2015
      192 pages
      ISBN:9781450338356
      DOI:10.1145/2808006

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 29 September 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      SIGITE '15 Paper Acceptance Rate24of58submissions,41%Overall Acceptance Rate176of429submissions,41%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader