Python may be part of the practical work
Students discussing the course mention learning data management with Python, which suggests that basic programming comfort can make the material easier to approach.
Rutgers catalogResearched guideSIRS history12 section records
04:547:221
Fundamentals Of Data Curation And Management
INFORMATION TECHNOLOGY AND INFORMATICS
- credits
- 3
- Core
- None listed
- Typical seasons
- Fall, Spring, Summer
- Fall 2026 sections
- 3
- Open snapshot
- 0 open in snapshot
Course guide
AI-generated overview
Course fingerprint 10 AI-generated signals
Difficulty
3/5Why
Students must clean, describe, visualize, and query numeric, text, and network data across multiple workflows.
Workload
4/5Why
Practical applications in three data modalities plus database and reporting work suggest regular applied assignments.
Pacing
3/5Why
The course moves from acquisition and cleaning into visualization, database work, and query language in one term.
Projects
4/5Why
Rutgers emphasizes practical applications, database solutions, and report creation rather than passive survey content.
Exams
2/5Why
Official materials focus much more on applied data tasks than on a clearly exam-centered grading structure.
Math
3/5Why
The course includes numeric data and visualization preparation, but it is broader than a math-heavy methods class.
Memorization
2/5Why
Students need workflow vocabulary like metadata and data quality, but usable practice seems more important than memorization.
Abstraction
3/5Why
The class mixes concrete data handling with conceptual thinking about what data means and how quality is assessed.
Prerequisites
4/5Why
Rutgers requires prior programming or a CS-plus-statistics path before students take this course.
Reading
2/5Why
The official description points more to doing data work across tools than to unusually heavy reading alone.
What students tend to say
Public Rutgers ITI discussions describe Data Curation and Management as a useful course for learning data management with Python, while also warning that it is less trivial for students who dislike coding or quantitative work. These are limited student reports, not a universal workload profile.
It is not only a theory elective
Public comments distinguish 221 from lighter research or information-retrieval electives and describe hands-on work with data; verify the current project and assignment mix.
The course is still instructor-dependent
The available discussion is sparse and often compares several ITI options rather than documenting one section, so treat it as a prompt to ask about the current syllabus.
Topic breakdown
A practical chapter-by-chapter view from foundations to applications.
4 modules
Module 1
Data acquisition
Students learn how datasets are found, collected, and prepared for practical work.
data sourcescollectionmetadata
Basic concept overview
Useful data is prepared data
Most data work begins before analysis, with cleaning and documentation.
Modality changes method
Text, networks, and numbers need different handling and assumptions.
Curation is a professional skill
Good data management makes later analysis more trustworthy and repeatable.
Things to watch for
Treating datasets as self-explanatory
Track source, collection process, variables, and limits.
Cleaning without preserving decisions
Document transformations so the dataset remains auditable.
Jumping to patterns too soon
Check quality and missingness before interpreting results.
Fall 2026 sections
0 open · College Avenue
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114502 | Closed | JHAVER, SHAGUN | Monday 2:00 PM-3:20 PM at CI 119; ONLINE INSTRUCTION(INTERNET)CI 119 | College Avenue |
| 0214503 | Closed | BRUNO, JAMES | Monday 3:50 PM-5:10 PM at CI 119; ONLINE INSTRUCTION(INTERNET)CI 119 | College Avenue |
| 0514506 | Closed | WOLFE, ROBERT | Tuesday 10:20 AM-11:40 AM at HH A6; Friday 10:20 AM-11:40 AMHH A6 | College Avenue |
cachedSource: Checked-in Rutgers Schedule of Classes snapshotsUpdated when term datasets are refreshedMay be stale
SIRS teaching signals
Historical student surveys
Teaching
4.19
Course quality
4.19
Response rate
65.4%
Coverage
12 offerings · 2017–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Jhaver, Shagun | 10 | 4.13 | 4.14 |
| Segal, Mark | 1 | 4.80 | 4.70 |
| Lesk Michael | 1 | Not available | Not available |
Course stats
Catalog and planning context
Credits
3
Current campuses
College Avenue
Current availability
0 open of 3
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
5
Prerequisites
((01:198:142 and 01:960:291) or (04:547:202))<em> OR </em> ((01:198:142 and 01:960:212) or (04:547:202))<em> OR </em> ((01:198:142 and 01:960:384) or (04:547:202))<em> OR </em> ((01:198:142 and 33:136:385) or (04:547:202))<em> OR </em> ((01:960:142 and 01:960:291) or (04:547:202))<em> OR </em> ((01:960:142 and 01:960:212) or (04:547:202))<em> OR </em> ((01:960:142 and 01:960:384) or (04:547:202))
Degree requirement lists
No direct degree-list membership appears in the checked-in requirement index.
