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Counting is model selection
Most early mistakes come from choosing the wrong model, not from arithmetic. The real question is whether order matters, repetition is allowed, or structure is being partitioned into cases.
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01:198:206 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
Introduction to Discrete Structures II gives Rutgers computer science students the combinatorics, recurrence, probability, and graph-theory background used later in algorithms, systems analysis, and related CS work.
Can I take it?
Introduction To Discrete Structures I · Calculus II For Mathematical And Physical Sciences · 01:640:192
How hard?
Students rate teaching
SIRS · 3,583 responses · 2014–2025
Spring 2027
4 open in snapshot · 1 instructor
When it meets
Busch
Checked-in sections · current seat counts are unavailable.
MTh 12:10 PM · Busch · 4 sections · 4 open
AI-generated overview
Higher means more demanding · AI summary
6 units · topic sequence may vary by instructor
The course usually opens by turning informal counting questions into clean combinatorial models. Students learn when order matters, when it does not, and how binomial coefficients, permutations, combinations, and partitions encode those choices.
After direct counting, the class typically shifts to ways of describing sequences and recursive structure. Recurrences and generating functions act like compression tools: they let you describe large families of counts without listing everything by hand.
This module reframes discrete math around uncertainty. The emphasis is on defining the sample space carefully, translating real statements into events, and then reasoning with conditional probability and independence instead of guessing from intuition.
Once events are in place, the course starts measuring numerical outcomes. Students work with random variables, expected value, variance, and standard deviation so they can summarize randomness and compare different processes quantitatively.
Official Rutgers materials highlight the binomial, Poisson, and geometric distributions, plus the law of large numbers and random walks in sample syllabi. This part of the course is where many students start seeing why the class supports algorithms, simulation, and systems work.
The graph-theory portion is usually compact but important. Rather than doing every graph topic in depth, the class focuses on the ideas Rutgers lists explicitly: paths, components, connectivity, Euler and Hamiltonian paths, planar graphs, and trees.
Public Rutgers discussion treats 206 as a core math-support course for CS that often feels more like applied counting and probability than a continuation of proof-heavy Discrete I. Students repeatedly describe success as very pacing-dependent: when they keep up with examples and quizzes, the class feels manageable; when they fall behind, it compounds quickly.
Most early mistakes come from choosing the wrong model, not from arithmetic. The real question is whether order matters, repetition is allowed, or structure is being partitioned into cases.
A recurrence works when a large problem can be expressed through smaller versions of itself. That perspective becomes useful again later in algorithms.
Expected value is not just a formula to memorize; it is the main way the course compresses many random outcomes into one interpretable number.
Graph theory turns relationships into objects that can be traversed, partitioned, and tested for properties like connectivity or special paths.
Common pitfall
Instead
Name the objects, the ordering rule, and whether repetition is allowed before reaching for combinations or permutations.
Common pitfall
Instead
Disjoint means they cannot happen together; independent means one does not change the probability of the other.
Common pitfall
Instead
Write the random variable first, then list its possible values and probabilities before computing the weighted average.
Common pitfall
Instead
Draw tiny graphs and test the property yourself: connected or not, Eulerian or not, tree or not.
4 open · Busch
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114423 | Open | Samaneh Hamidi | Monday 12:10 PM-1:30 PM at HLL 114; Thursday 12:10 PM-1:30 PM at HLL 114; Wednesday 8:45 AM-9:40 AM at SEC 202HLL 114SEC 202 | Busch |
| 0214424 | Open | Samaneh Hamidi | Monday 12:10 PM-1:30 PM at HLL 114; Thursday 12:10 PM-1:30 PM at HLL 114; Wednesday 7:45 PM-8:40 PM at BME 102HLL 114BME 102 | Busch |
| 0314425 | Open | Samaneh Hamidi | Monday 12:10 PM-1:30 PM at HLL 114; Thursday 12:10 PM-1:30 PM at HLL 114; Tuesday 8:45 AM-9:40 AM at SEC 203HLL 114SEC 203 | Busch |
| 0414426 | Open | Samaneh Hamidi | Monday 12:10 PM-1:30 PM at HLL 114; Thursday 12:10 PM-1:30 PM at HLL 114; Friday 4:05 PM-5:00 PM at SEC 205HLL 114SEC 205 | Busch |
Historical student surveys
Teaching
3.91
Course quality
3.74
Response rate
57.5%
Coverage
49 offerings · 2014–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Samaneh Gholizadeh Hamidi | 14 | 3.73 | 3.60 |
| Konstantinos Michmizos | 5 | 4.58 | 4.36 |
| Charles Cowan | 3 | 4.37 | 4.03 |
| Cash David | 2 | 4.80 | 4.60 |
| Weidenhoft J | 2 | 4.20 | 4.25 |
| Eric Allender | 2 | 3.90 | 3.90 |
Catalog and planning context
Credits
4
Current campuses
Busch
Current availability
4 open of 4
Catalog terms
Fall, Spring, Summer
Core codes
None listed
Loaded terms
6
((01:198:205 or 14:332:312) and (01:640:152))<em> OR </em> ((01:198:205 or 14:332:312) and (01:640:192))
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