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Data literacy is disciplined skepticism
The course is built around not being fooled by compelling but weak numerical stories. A good answer is one that survives questions about sampling, randomness, and framing.
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01:960:142 · Statistics · School 01 — New Brunswick School of Arts and Sciences
Data 101 is a four-credit data-literacy introduction that teaches students to reason with data, use R for analysis and visualization, and think critically about randomness, evidence, privacy, and the social consequences of data-driven claims.
How hard?
Students rate teaching
SIRS · 303 responses · 2016–2025
Spring 2027
2 open in snapshot · 1 instructor
When it meets
Livingston
Checked-in sections · current seat counts are unavailable.
AI-generated overview
Higher means more demanding · AI summary
5 units · topic sequence may vary by instructor
The opening stretch is about getting comfortable with R and learning that data work starts with a question, not with a chart. Rutgers' official course materials frame the class as an accessible starting point for non-programmers, so early weeks usually focus on simple commands, importing data, and seeing how a dataset answers only certain kinds of questions well.
Once students can manipulate data a little, the course shifts into visual and exploratory reasoning. The emphasis is less on polished dashboard work and more on noticing patterns, creating useful derived attributes, and recognizing that the way data are transformed can change the story a chart seems to tell.
The middle of the course introduces the probability ideas needed to argue with data instead of just describing it. Official weekly topics point to normal distributions, the central limit theorem, permutation tests, and z-tests, which together teach students why random variation can mimic patterns and why formal evidence standards matter.
Data 101 is unusually explicit about the difference between mathematically correct output and sound judgment. The official schedule calls out multiple hypothesis testing, Bayesian reasoning, and cognitive traps, so this part of the course tends to connect statistics to real-world reasoning errors, especially when many comparisons or emotionally loaded claims are involved.
Later weeks tie the technical material back to social claims. Rutgers' published calendar mentions recursive partitioning, linear regression, Simpson's paradox, ecological fallacy, prosecutorial fallacy, and power-law behavior, which makes the course feel like a capstone on how data can clarify a question or badly mislead it depending on context.
Public r/rutgers discussion usually describes Data 101 as approachable for non-programmers and more interesting than a formula-heavy intro statistics class, but also more dependent on course organization, group dynamics, and the instructor's pacing than a standard lecture course.
The course is built around not being fooled by compelling but weak numerical stories. A good answer is one that survives questions about sampling, randomness, and framing.
Learning R matters because it lets students work with real datasets, but the deeper goal is understanding why a certain transformation, graph, or test supports a claim.
A graph can look dramatic even when the underlying pattern is mostly noise. The probability topics in the course teach when surprise deserves a formal test.
The same data pattern can imply different things depending on how the data were gathered, what population they represent, and what incentives shaped the original measurement.
Common pitfall
Instead
Always translate the output back into a plain-language claim about the data, the population, and the assumptions behind the method.
Common pitfall
Instead
Use the course's probability tools to separate real signal from what could easily happen by chance.
Common pitfall
Instead
The course repeatedly pushes students to ask where the data came from, what is missing, and whether the measurement process itself introduces bias.
2 open · Livingston
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114369 | Open | Imielinski | Tuesday 12:10 PM-1:30 PM at BE 100; Friday 12:10 PM-1:30 PM at BE 100; Wednesday 12:25 PM-1:20 PM at TIL 105BE 100TIL 105 | Livingston |
| 0214372 | Open | Imielinski | Tuesday 12:10 PM-1:30 PM at BE 100; Friday 12:10 PM-1:30 PM at BE 100; Wednesday 7:45 PM-8:40 PM at TIL 105BE 100TIL 105 | Livingston |
Historical student surveys
Teaching
3.88
Course quality
3.87
Response rate
33.0%
Coverage
16 offerings · 2016–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Tomasz Imielinski | 7 | 3.89 | 3.99 |
| Lmielinski T. | 2 | 4.00 | 4.10 |
| Imielinski Tomasz | 2 | 4.05 | 3.95 |
| Amelie Marian | 2 | 3.85 | 3.55 |
| Ta | 2 | 3.50 | 3.45 |
| Imielinski T | 1 | 4.20 | 4.20 |
Catalog and planning context
Credits
4
Current campuses
Livingston
Current availability
2 open of 2
Catalog terms
Spring
Core codes
CCO, ITR, QQ, QR
Loaded terms
3
Any Course EQUAL or GREATER Than: (01:640:025)
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