Course Detail

Big Data (41201)

Course Description by Faculty

  • Rockova, Veronika
  • Content
    BUS 41201 is a course about data mining: the analysis, exploration, and simplification of large high-dimensional datasets. Students will learn how to model and interpret complicated `Big Data' and become adept at building powerful models for prediction and classification.

    Techniques covered include an advanced overview of linear and logistic regression, model choice and false discovery rates, multinomial and binary regression, classification, decision trees, factor models, clustering, the bootstrap and cross-validation. We learn both basic underlying concepts and practical computational skills, including techniques for analysis of distributed data.

    Heavy emphasis is placed on analysis of actual datasets, and on development of application specific methodology. Among other examples, we will consider consumer database mining, internet and social media tracking, network analysis, and text mining.

    Format
    • Lectures

    • Discussion

    • Group Projects

    • Group Presentations

  • Prerequisites
    Bus 41000 (or 41100). Cannot enroll in BUSN 41201 if BUSN 20800 taken previously.
  • Materials
    This course will have a Canvas site.
    Resources
    • Canvas Site Available

  • Grades
    Individual: 30% take-home Midterm exam

    Group: 30% weekly homework, 40% final project

    Grades
    • Graded homework assignments

    Assessment & Testing
    • Midterm

    • Final exam (take home)

  • Syllabus
  • Spring 2023Section: 41201-01W 8:30AM-11:30AMHarper CenterC05In-Person Only
  • Spring 2023Section: 41201-02W 1:30PM-4:30PMHarper CenterC05In-Person Only
  • Spring 2023Section: 41201-81W 6:00PM-9:00PMGleacher Center306In-Person Only
Description and/or course criteria last updated: June 8 2022