ALY 4020 — Predictive Analytics Using R and Python

3 semester hoursUndergraduateLectureusually offered: fallBostonOnlineOnlineTraditional

Introduces the end-to-end data-driven predictive modeling approach in R, Python, KNIME and WEKA with applications and case studies. Includes all the data and modeling steps in a full modeling cycle (training, validation and testing), exploratory data analysis and data cleansing, commonly applied modeling techniques such as SVM, random forest and ensemble models; introduces neural networks using TensorFlow.

Prerequisites

one of:
- all of:
  - ALY 2100 (min D-)
  - ALY 3015 (min D-)
- ALY 3040 (min D-)

Offering history

Term	Sections	Enrolled	Capacity	Full	Open seats/section
Fall 2025	2	8	60	13%	26.0

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 0% · T 0% · W 38% · Th 0% · F 0%

Common patterns: async (63% of sections), W (38% of sections) — in patterns, R means Thursday

Professors

Fall

Vladimir Shapiro (100% of students) · reviews

Percentages are each professor's average share of the season's enrolled students in recent terms.

Unlocks

ALY 4520

Courses that list ALY 4020 in their prerequisites.

Links

Official catalog (ALY course descriptions) · All ALY courses · Plan it at numap.app