BZAN 7706 Data Analytics 1: Model Building

Machine Learning, big data, data mining, predictive analytics. These are what the course covers. They consist of the creation and use of mathematical computer models to predict important quantities and events with uncanny accuracy. As one book put it, " Who clicks, who buys, and who dies." The course teaches both the principles and the details of the major methods of making and applying these models to actual business problems. To produce models on the computer, students also learn the R coding language. This is the preferred high-level software for Machine Learning and statistical applications. STEM-designated

Course overview

Department
Business Analytics
School
CSOM
Credits
3
Level
Graduate
Offered
Every Fall, Every Spring, Every Summer

Catalog details

Prerequisites
BZAN7703

Catalog text is imported from the reviewed Boston College course snapshot. Confirm eligibility in EagleApps.

Course evaluations

Response-weighted averages from Boston College’s official course evaluations (Avalanche/Blue).

Course rating
4.12 / 5 · Very Good
537 responses
Instructor rating
4.39 / 5 · Very Good
537 responses
Terms evaluated
14
Most recent: Spring 2026

Scales: overall ratings run from 1 (poor) to 5 (excellent); agreement questions from 1 (strongly disagree) to 5 (strongly agree); weekly effort from 1 (up to 1 hour) to 5 (10 or more hours).

Evaluations by term and instructor
TermInstructorCourse overallInstructor overallWell organizedClear learning objectivesIntellectually challengingHelpful assignmentsApplicable beyond courseAttendance necessaryWeekly effortResponses
Spring 2026Pieter Vanderwerf4.04.44.74.74.54.34.44.14–6 hrs/week40 of 46
Fall 2025Pieter Vanderwerf4.04.44.74.74.54.54.34.64–6 hrs/week40 of 44
Summer 2025Pieter Vanderwerf4.14.24.44.54.64.64.24.44–6 hrs/week18 of 21
Spring 2025Pieter Vanderwerf4.14.54.74.74.54.54.24.54–6 hrs/week43 of 45
Fall 2024Pieter Vanderwerf4.54.64.94.84.84.64.54.54–6 hrs/week46 of 52
Summer 2024Pieter Vanderwerf4.24.64.84.84.74.54.24.64–6 hrs/week23 of 28
Spring 2024Pieter Vanderwerf4.44.74.74.84.64.84.34.64–6 hrs/week54 of 61
Fall 2023Pieter Vanderwerf—————————55 of 63
Summer 2023Pieter Vanderwerf—————————17 of 28
Spring 2023Pieter Vanderwerf4.24.54.84.74.74.34.34.61–3 hrs/week51 of 63
Fall 2022Jiri Chod3.94.14.64.54.44.44.34.24–6 hrs/week38 of 43
Fall 2022Pieter Vanderwerf4.04.34.54.44.64.43.84.71–3 hrs/week19 of 22
Summer 2022Pieter Vanderwerf3.74.04.84.64.74.43.85.010+ hrs/week9 of 32
Spring 2022Pieter Vanderwerf3.94.24.54.54.64.44.24.34–6 hrs/week40 of 81
Fall 2021Jiri Chod4.04.14.54.44.54.44.34.64–6 hrs/week44 of 84

Data freshness

Course and evaluation data last updated 2026-10-09. Source details and limitations are documented in Data Sources and Methodology.

Instructors

  • Jiri Chod
    Fall 2022, Fall 2021 · Official rating 4.09/5
  • Pieter Vanderwerf
    Spring 2027, Fall 2026, Summer 2026, Spring 2026, Fall 2025, Summer 2025, Spring 2025, Fall 2024, Summer 2024, Spring 2024, Fall 2023, Summer 2023, Spring 2023, Fall 2022, Summer 2022, Spring 2022 · Official rating 4.45/5

Sections

  • Section 01
    Spring 2027 · Pieter Vanderwerf · On-line Asynchronous · Offered
  • Section 02
    Spring 2027 · Pieter Vanderwerf · Fulton Hall 423 M 07:00PM-09:30PM · Offered
  • Section 01
    Fall 2026 · Pieter Vanderwerf · On-line Asynchronous · Offered
  • Section 02
    Fall 2026 · Pieter Vanderwerf · Fulton Hall 245 W 07:00PM-09:30PM · Offered
  • Section 01
    Summer 2026 · Pieter Vanderwerf · On-line Asynchronous · Offered