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01:198:439 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
Introduction to Data Science is a survey-style applied course on preparing, visualizing, analyzing, and building with data, covering data wrangling, statistical foundations, machine learning, data mining, and application-oriented workflows.
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Introduction To Discrete Structures I · Calculus II For Mathematical And Physical Sciences · 01:640:192
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Students rate teaching
SIRS · 863 responses · 2018–2025
Spring 2027
7 open in snapshot · 2 instructors
When it meets
Busch
Checked-in sections · current seat counts are unavailable.
W 10:20 AM · Busch · 4 sections · 4 open
AI-generated overview
Higher means more demanding · AI summary
6 units · topic sequence may vary by instructor
Recent Rutgers course pages frame the opening weeks as a practical setup phase: Python, notebooks or scripts, and the basic libraries used to inspect and move through data before any machine-learning model appears.
A large early block is about getting data into usable form. The official topic lists and Spring 2025 schedule both emphasize integration, scraping or collection, cleaning, transformation, and text-oriented preprocessing as real data-science work rather than busywork.
Before predictive models, the course usually trains students to look at the data well. Visualization, exploratory data analysis, probability, distributions, and basic statistics serve as the language for understanding what a dataset is actually saying.
The middle of the course typically adds just enough mathematical machinery to support modern modeling. Recent Rutgers schedules explicitly mention linear algebra, matrix decompositions, SVD, PCA, and dimensionality reduction.
The largest modeling block covers predictive learning from labeled data. Rutgers topic lists call out regression, k-nearest neighbors, decision trees, naive Bayes, support vector machines, ensemble methods, and evaluation techniques.
Later units move into unsupervised or product-facing topics such as clustering, recommender systems, anomaly detection, and, on the Spring 2025 Rutgers site, a short introduction to deep learning and large language models.
Public r/rutgers discussion tends to describe 439 as a broad, useful survey with a noticeable machine-learning flavor. Compared with AI, students often frame it as the more approachable or directly career-relevant elective, though the exact balance of coding, theory, and projects can shift by instructor.
Rutgers' official description puts collection, integration, and preprocessing right next to modeling, which is a good clue that the course treats wrangling as central work, not an optional prelude.
Encoding, cleaning, dimensionality reduction, and feature engineering often matter as much as the final algorithm choice.
A classifier or regressor is only meaningful when you can judge its fit, generalization, and failure modes with the right metrics or validation setup.
The course bridges mathematical ideas, programming tools, and application questions, so students practice moving between theory and implementation.
Common pitfall
Instead
Inspect missing values, scales, distributions, and label quality first so the modeling stage is answering the right question.
Common pitfall
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Different data types need different transformations, and those choices directly shape what later models can learn.
Common pitfall
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For each method, practice stating what kind of target, features, geometry, or probabilistic story it expects.
Common pitfall
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Interpret metrics alongside class balance, task goals, and whether the split or validation setup matches the real use case.
7 open · Busch
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114518 | Open | Gunawardena | Wednesday 10:20 AM-11:40 AM at SEC 111; Friday 3:50 PM-5:10 PM at SEC 111; Wednesday 5:55 PM-6:50 PM at SEC 207SEC 111SEC 207 | Busch |
| 0214519 | Open | Gunawardena | Wednesday 10:20 AM-11:40 AM at SEC 111; Friday 3:50 PM-5:10 PM at SEC 111; Wednesday 7:45 PM-8:40 PM at SEC 207SEC 111SEC 207 | Busch |
| 0314520 | Open | Gunawardena | Wednesday 10:20 AM-11:40 AM at SEC 111; Friday 3:50 PM-5:10 PM at SEC 111; Wednesday 12:25 PM-1:20 PM at SEC 207SEC 111SEC 207 | Busch |
| 0514522 | Open | Ruixiang Tang | Wednesday 10:20 AM-11:40 AM at ARC 103; Friday 3:50 PM-5:10 PM at ARC 103; Wednesday 5:55 PM-6:50 PM at SEC 205ARC 103SEC 205 | Busch |
| 0614523 | Open | Ruixiang Tang | Wednesday 10:20 AM-11:40 AM at ARC 103; Friday 3:50 PM-5:10 PM at ARC 103; Wednesday 4:05 PM-5:00 PM at SEC 203ARC 103SEC 203 | Busch |
| 0714524 | Open | Ruixiang Tang | Wednesday 10:20 AM-11:40 AM at ARC 103; Friday 3:50 PM-5:10 PM at ARC 103; Wednesday 2:15 PM-3:10 PM at SEC 202ARC 103SEC 202 | Busch |
| 0814525 | Open | Ruixiang Tang | Wednesday 10:20 AM-11:40 AM at ARC 103; Friday 3:50 PM-5:10 PM at ARC 103; Wednesday 12:25 PM-1:20 PM at SEC 202ARC 103SEC 202 | Busch |
Historical student surveys
Teaching
3.86
Course quality
3.79
Response rate
32.0%
Coverage
18 offerings · 2018–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Ananda Gunawardena | 11 | 3.99 | 3.95 |
| Sai Samhith Thatikonda | 1 | 4.20 | 4.20 |
| Ruixiang Tang | 1 | 4.20 | 4.10 |
| Yongfeng Zhang | 1 | 4.00 | 3.80 |
| Demelo Gerard | 1 | 3.50 | 3.50 |
| Ananda Gunawardena | 1 | 3.50 | 3.40 |
Catalog and planning context
Credits
4
Current campuses
Busch
Current availability
7 open of 7
Catalog terms
Fall, Spring
Core codes
None listed
Loaded terms
5
((01:198:205 or 14:332:202 or 14:332:312) and (01:640:152)) <em> OR </em> ((01:198:205 or 14:332:202 or 14:332:312) and (01:640:192))
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