Course Detail (Course Description By Faculty)

Business Statistics (41000)

For autumn quarter, this class will be offered in dual mode but it is possible that the faculty member will not be in the classroom.

Bus41000 is a course on Data Analytics. Students will learn how to visualize and analyze data together with learning model building skills for analysis and prediction.

This course provides an introduction to key concepts and data analytic tools that are applicable in business environments. Both basic underlying concepts and practical computational skills are covered.

The techniques covered include (i) graphical data visualization; (ii) Bayes Probability; (iii) Statistical Modeling and Inference; (iv) A/B testing; (v) linear, logistic and multiple regression.

This class uses R which is widely used in data science. Students are encouraged to familiarize themselves with the links and code available on the course website.
None.
There are no required texts. Detailed lecture notes are available on the Course Website.
Based on a midterm and a final project.
Description and/or course criteria last updated: September 18 2023
SCHEDULE
  • Autumn 2023
    Section: 41000-03
    W 8:30 AM-11:30 AM
    Harper Center
    C04
    In-Person Only
  • Autumn 2023
    Section: 41000-85
    S 9:00 AM-12:00 PM
    Gleacher Center
    406
    In-Person Only

Business Statistics (41000) - Polson, Nicholas>>

For autumn quarter, this class will be offered in dual mode but it is possible that the faculty member will not be in the classroom.

Bus41000 is a course on Data Analytics. Students will learn how to visualize and analyze data together with learning model building skills for analysis and prediction.

This course provides an introduction to key concepts and data analytic tools that are applicable in business environments. Both basic underlying concepts and practical computational skills are covered.

The techniques covered include (i) graphical data visualization; (ii) Bayes Probability; (iii) Statistical Modeling and Inference; (iv) A/B testing; (v) linear, logistic and multiple regression.

This class uses R which is widely used in data science. Students are encouraged to familiarize themselves with the links and code available on the course website.
None.
There are no required texts. Detailed lecture notes are available on the Course Website.
Based on a midterm and a final project.
Description and/or course criteria last updated: September 18 2023
SCHEDULE
  • Autumn 2023
    Section: 41000-03
    W 8:30 AM-11:30 AM
    Harper Center
    C04
    In-Person Only
  • Autumn 2023
    Section: 41000-85
    S 9:00 AM-12:00 PM
    Gleacher Center
    406
    In-Person Only