IS 507 Data, Statistical Models, and Information

An introduction to statistical and probabilistic models as they pertain to quantifying information, assessing information quality, and principled application of information to decision making, with focus on model selection and gauging model quality. The course reviews relevant results from probability theory, parametric and non-parametric predictive models, as well as extensions of these models for unsupervised learning. Applications of statistical and probabilistic models to tasks in information management (e.g. prediction, ranking, and data reduction) are emphasized.

Previously IS542.

  • Required core course – MS in Information Management

Recent syllabus

Scheduled Offerings

  • Fall 2020

    • IS507AC - Data, Stat, Info
      Mon, 1:00 pm - 3:50 pm
      Room 126
      On-Campus
      Graduate student questions may be sent to ischool-advising@illinois.edu
      Instructor
      Philip Bosch
      CRN
      68976
      Length
      16 weeks
      Credit hours
      4
    • IS507AO - Data, Stat, Info
      Wed, 6:00 pm - 8:00 pm
      Online
      Graduate student questions may be sent to ischool-advising@illinois.edu
      Instructor
      Jill Naiman
      CRN
      68974
      Length
      16 weeks
      Credit hours
      4
    • IS507BC - Data, Stat, Info
      Tue, 12:30 pm - 3:20 pm
      Architecture Building 302
      On-Campus
      Graduate student questions may be sent to ischool-advising@illinois.edu
      Instructor
      Victoria Stodden
      CRN
      70324
      Length
      16 weeks
    • IS507CC - Data, Stat, Info
      On-Campus
      CRN
      73151
      Length
      16 weeks
  • Spring 2020

    • IS542A - Data, Stat, Info
      Fri, 2:00 pm - 4:50 pm
      Room 46
      On-Campus
      This is a required course for the MSIM degree.
      Instructor
      Philip Bosch
      CRN
      67383
      Length
      16 weeks
    • IS542AO - Data, Stat, Info
      Wed, 6:00 pm - 8:00 pm
      Online
      This is a required course for the MSIM degree.
      Instructor
      Jill Naiman
      CRN
      69161
      Length
      16 weeks

Textbooks and Course Materials

  • Articulate the role of marginal, joint, and conditional probability in modeling

    processes involving information.

  • Select, parameterize, and compare probability distributions as vehicles for

    modeling information.

  • Specify, estimate and evaluate elementary parametric and non-parametric statistical models.