Start with Thinking, Not Typing
Early on, many of us try to “learn to code” by collecting snippets. I watched a classmate keep a binder of Java fragments, and the moment the assignment changed a tiny detail, the whole approach fell apart.
Switch the workflow:
- Write the goal in one sentence.
- Draft pseudocode on paper (no syntax allowed).
- Translate into code and run tiny tests.
Mini-layout (FizzBuzz, the humane way)
Pseudocode
for each number from 1..100:
if divisible by 15 → print "FizzBuzz"
else if divisible by 3 → "Fizz"
else if divisible by 5 → "Buzz"
else print number
Now translate (Python)
for n in range(1, 101):
out = "Fizz"(n%3==0) + "Buzz"(n%5==0)
print(out or n)
“Boring” Fundamentals Pay the Rent
I tried to build a chess bot before I knew what a tree was. Predictable disaster. After a weekend with depth-first search and evaluation functions, the same idea made sense.
- Arrays, lists, dicts/maps (access patterns)
- Stacks, queues, trees, graphs (relationships)
- Recursion vs. iteration (call stack costs)
- Big-O reasoning (why a hash map beats a list for lookups)
Fact you can feel: A lookup in a Python list is O(n). A lookup in a dict averages O(1). Replacing a looped search with a dict turns a sluggish script into a snappy one. And remember, you always can find Computer Science Assignment Help.
Make Errors Your Teammates
Error messages are not judgement; they’re directions. Read the first one. Reproduce the bug in the smallest example you can make.
Crash course in debugging steps
- Reproduce reliably (same input, same crash).
- Isolate by deleting code until the problem stays but the file shrinks.
- Inspect variables with prints or a debugger (pdb / VS Code breakpoints).
- Fix one thing only; re-run; commit.
Python: off-by-one demo
nums = [10,20,30]
for i in range(len(nums)): # OK, but watch boundaries
print(nums)
If you see IndexError, print i, len(nums) and the failing index.
Pro-tip: When a stack trace is long, the useful line is usually the last frame of your file, not the top of the library noise.
Use Git Before You Think You Need Git
Everyone has a folder full of “final_v2_realfinal.py” at least once. Version control ends that chapter.
Day-one Git layout
git init
git add .
git commit -m "First snapshot"
After a working change:
git commit -am "Implement input validation"
Oops?
git log --oneline
git checkout <good_commit_hash>
- Commit small, commit often (makes blame painless).
- Write messages your future self can understand.
- Push to a private repo; protect your academic integrity.
Borrow Code the Right Way
Copying from answers without reading breeds panic during viva or code walk-throughs. Borrow — then rewrite.
Two-pass method
- Paste into a sandbox. Above each block, write one sentence explaining it.
- Close the tab. Rebuild the logic in a fresh file from memory.
Example comment:
"This function returns Fibonacci using memoization to avoid repeated work."
def fib(n, memo=None):
memo = memo or {}
if n <= 1: return n
if n in memo: return memo[n]
memo[n] = fib(n-1, memo) + fib(n-2, memo)
return memo[n]
nding builds grit; collaboration builds insight. The first time you explain your loop invariant to a friend and catch your own bug mid-sentence, you’ll see why.
Low-friction ways to collaborate
- Pair for 45 minutes: one types, one narrates.
- Trade code for a 5-minute review (one suggestion each).
- Join a campus or Discord study room; ask “what am I missing?” not “do it for me.”
Document as You Go (Future You Will Thank You)
A project fails at integration more than at algorithms. Comments, docstrings, and a short README prevent 3 a.m. disasters.
example_module.py
def normalize(scores):
"""
Scale scores to 0..1.
Args: scores (list[float])
Returns: list[float]
"""
if not scores: return []
m, M = min(scores), max(scores)
return [(s - m) / (M - m or 1.0) for s in scores]
- One README section: “How to run,” “Assumptions,” “Examples.”
- Docstrings for every function someone else might call.
Measure What Matters (Tiny Benchmarks Beat Vibes)
“My code feels slow” is not a metric. Time a function on realistic inputs, then optimize the part that dominates.
Python: quick timing
import time
start = time.perf_counter()
... run function ...
elapsed = time.perf_counter() - start
print(f"{elapsed:.4f}s")
Micro-win: Replace a repeated list search with a dict set-membership check; watch runtime drop on large inputs.
Adopt the “Forever Beginner” Mindset
CS doesn’t end. You won’t “finish” algorithms or databases. Skills stack. Curiosity compounds. The graduates who keep asking why are the ones who stop getting stuck.
- Keep a “question log” when something confuses you.
- After solving, write a 3-line summary: problem → approach → trade-offs.
Practice Pack (Use Tonight)
- Pick one old assignment and rewrite it with comments you’d be proud to show a TA.
- Create a private Git repo; commit your current draft; add a README with run steps.
- Benchmark one function (before/after a small optimization).
- Explain your solution to a friend in plain language; note the question they ask first.
Stretch goal: Rebuild one copied solution from memory. If you can, you learned it. If you can’t, you found tomorrow’s study target.
Notes
- Big-O intuition: If doubling input roughly doubles runtime, you’re near O(n). If it explodes, you’re flirting with O(n²). Hash maps/dicts help when you do lots of membership checks.
- Academic integrity: Keep your repos private; never share graded solutions publicly. Learn from others, credit sources, and be able to explain every line you submit.
