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Data literacy is about judgment, not just software
R matters because it gives students a practical tool, but the larger goal is learning when a claim from data is persuasive, weak, rushed, or misleading.
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01:198:142 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
Data 101 is an introductory data-literacy course built around R, weekly data puzzles, practical statistics, and the social impact of data, with an emphasis on learning how to make and defend claims from real datasets.
How hard?
Students rate teaching
SIRS · 1,569 responses · 2016–2025
Spring 2027
8 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
6 units · topic sequence may vary by instructor
The official weekly outline starts with orientation to data science and setting up R, then moves quickly into simple queries and basic manipulation. The point is not to become a software engineer, but to get comfortable enough with a real analysis environment that you can ask concrete questions of data.
Early weeks focus on seeing patterns in data. Students visualize distributions, transform raw columns into more useful derived fields, and learn that cleaning and re-framing data is often as important as any formal statistical test.
The middle of the course introduces common traps: the law of small numbers, confusing databases with analysis, and jumping from a noisy sample to a strong conclusion. This is where the course's "healthy skepticism" theme becomes concrete.
From there the course moves into the main inference toolkit: normal distributions, the central limit theorem, permutation tests, z-tests, and then the complications introduced by multiple testing. The emphasis is on reasoning persuasively with data, not memorizing every possible formula.
The official outline then blends probability judgment with communication. Students study Bayesian reasoning and common judgment errors while also defending weekly puzzle solutions or social-data arguments in the course's "Court of Data" presentation format.
The later weeks introduce predictive modeling tools such as recursive partitioning and linear regression, then end with classic ways data can mislead, including Simpson's paradox, ecological fallacy, prosecutorial fallacy, and power-law behavior.
Public Rutgers discussion usually describes Data 101 as approachable for students without a deep coding background, especially because it teaches practical R and focuses on interpretation more than on advanced theory. At the same time, some students say the class can feel surface-level or unevenly organized depending on the semester, so it works best when you treat the weekly puzzles and discussions as the real center of the course.
R matters because it gives students a practical tool, but the larger goal is learning when a claim from data is persuasive, weak, rushed, or misleading.
A graph or derived attribute can suggest an interesting pattern, but the course keeps returning to the question of whether that pattern survives more careful statistical reasoning.
Healthy skepticism in this class means recognizing sampling noise, multiple comparisons, cognitive biases, and the many ways a persuasive-looking chart can still support a bad conclusion.
The Court of Data format makes a core point of the class explicit: a data result is only useful when you can explain the evidence, defend the method, and state the limits of the claim.
Common pitfall
Instead
Connect each command to an analytical goal: what question you are asking, what transformation you are making, and what conclusion the output actually supports.
Common pitfall
Instead
Keep the course's skepticism theme in mind: significance is only one piece of evidence, and design, assumptions, context, and effect size still matter.
Common pitfall
Instead
Many official examples are about how data affects real people, so ask who collected the data, what is omitted, and what incentives may shape the conclusion.
8 open · Livingston
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114368 | 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 |
| 0214370 | 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 251BE 100TIL 251 | Livingston |
| 0314371 | 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 |
| 0414373 | 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 103ABE 100TIL 103A | Livingston |
| 0514374 | Open | Imielinski | Tuesday 12:10 PM-1:30 PM at BE 100; Friday 12:10 PM-1:30 PM at BE 100; Thursday 7:45 PM-8:40 PM at TIL 103BBE 100TIL 103B | Livingston |
| 0614375 | Open | Imielinski | Tuesday 12:10 PM-1:30 PM at BE 100; Friday 12:10 PM-1:30 PM at BE 100; Thursday 7:45 PM-8:40 PM at BE 121BE 100BE 121 | Livingston |
| 0714376 | Open | Imielinski | Tuesday 12:10 PM-1:30 PM at BE 100; Friday 12:10 PM-1:30 PM at BE 100; Wednesday 4:05 PM-5:00 PM at LSH B112BE 100LSH B112 | Livingston |
| 0814377 | Open | Imielinski | Tuesday 12:10 PM-1:30 PM at BE 100; Friday 12:10 PM-1:30 PM at BE 100; Wednesday 4:05 PM-5:00 PM at BE 111BE 100BE 111 | Livingston |
Historical student surveys
Teaching
3.65
Course quality
3.64
Response rate
43.1%
Coverage
40 offerings · 2016–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Tomasz Imielinski | 17 | 3.83 | 3.76 |
| Ta | 8 | 3.80 | 3.91 |
| Amelie Marian | 4 | 3.85 | 3.60 |
| Jack Mardekian | 4 | 3.10 | 3.15 |
| Samaneh Gholizadeh Hamidi | 3 | 2.13 | 2.43 |
| Imielinski Tomasz | 2 | 4.05 | 3.95 |
Catalog and planning context
Credits
4
Current campuses
Livingston
Current availability
8 open of 8
Catalog terms
Fall, Spring, Summer
Core codes
CCO, ITR, QQ, QR
Loaded terms
6
Any Course EQUAL or GREATER Than: (01:640:025)
No direct degree-list membership appears in the checked-in requirement index.