Spring 2027

Cloud Analytics and Artificial Intelligence Tools for Business

Applies statistics and data literacy to business problems using cloud-based analytics and AI tools.

offering recorded2 credits
Recorded instructors · Spring 2027 Bipin Karunakaran3.6/5

Summary

1 / 6

Past student reviews · Bipin Karunakaran

Some students described the class as easy and noted that Professor Karunakaran’s lectures were not test-heavy, though others found the material challenging to follow.

What you’ll be able to do

  • Build and share dashboards using data visualization tools.

  • Utilize SQL to query cloud databases and warehouses.

  • Build predictive models in the cloud.

  • Extract insights and answer business questions utilizing generative AI in the cloud.

Source: UW Guide

Grade history

↗
average GPA
letter grades
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AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

GEN BUS 307, 317,ECON 400, 410, or declared in undergraduate Business Exchange program

GENBUS 308

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.50/5 from 8 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 3.3/5 raw quality · 2.6/5 difficulty · 7 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Bipin Karunakaran

Students found Bipin Karunakaran's course hands-on and fun, focusing on practical coding and data analysis. He was described as friendly and patient, making machine learning concepts simple and achievable while helping students apply skills to real-world business contexts.

Conversely, others criticized Karunakaran for being unorganized and a poor lecturer, noting that code demonstrations often failed and lectures were hard to follow. One student mentioned that grades relied heavily on unpredictable quizzes covering material that was hardly mentioned in class.

Recent recorded grades — Fall 2025: 3.92 GPA, 95.5% A/AB (n=89 letter grades); Spring 2026: 3.71 GPA, 83.5% A/AB (n=115 letter grades).

Historical instructors & teaching patterns

BIPIN KARUNAKARAN is recorded teaching in Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 750
DIS 301Classroom Instruction0 / 750
LEC 002Classroom Instruction0 / 750
DIS 302Classroom Instruction0 / 750

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Bipin Karunakaran

The course emphasized practical, real-world business applications, with students finding the hands-on coding and data projects useful for professional skill-building.

Recent recorded grades — Spring 2025: 3.69 GPA, 84.6% A/AB (n=26 letter grades); Fall 2025: 3.92 GPA, 95.5% A/AB (n=89 letter grades); Spring 2026: 3.71 GPA, 83.5% A/AB (n=115 letter grades).

difficulty & workload

Past student reviews · Bipin Karunakaran

Some students described the class as easy and noted that Professor Karunakaran’s lectures were not test-heavy, though others found the material challenging to follow.

Past student reviews · Bipin Karunakaran

Feedback on Professor Karunakaran was mixed; some students found him unorganized and his code hard to follow, while others appreciated his patience, friendliness, and willingness to re-explain concepts.

Topics

  • Statistics and data literacy applied to business
  • Data visualization and dashboard creation
  • SQL querying of cloud databases and warehouses
  • Cloud-based predictive modeling
  • Generative AI for business insights

Skills

  • Building and sharing dashboards with data visualization tools
  • Querying cloud databases and warehouses using SQL
  • Building predictive models in the cloud
  • Using generative AI in the cloud to extract insights and answer business questions

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
GENBUS 308 · Spring 2027

Cloud Analytics and Artificial Intelligence Tools for Business

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:231:026555",
    "course_id": "GENBUS 308",
    "course_uid": "course_02d7a8ec033448f8518d0cd4",
    "term_id": "1274",
    "source_course_id": "026555",
    "source_subject_id": "231",
    "title": "Cloud Analytics and Artificial Intelligence Tools for Business",
    "credits_min": 2,
    "credits_max": 2,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "403101e42f4cfea19a39e9ca0cf5a381d3e2f7fbad6a5499d2653a3ea7875d0d",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}