How to Actually Study This: One Course + ~50 Problems

CS1 to CS4 gave you the concepts. This lesson is the operational plan: spend ~4 weeks part-time and stop. The trap is treating CS as a bottomless pit; the way out is a specific, finite plan with clear "I'm done" criteria.

The plan in one paragraph

Pick one structured DSA course in Python. Finish it. Then solve about 50 LeetCode problems, filtered to ones that map to scraper work. Skip 70% of what a typical CS interview prep list contains. Total time: 30 to 80 hours over 4 to 6 weeks at evenings-and-weekends pace. At the end of it you'll be fluent enough to read Scrapy's internals, pass a scraper-job interview, and never trip the O(n²) bug.

Step 1: pick one course, finish it

The single most common failure mode here is starting five courses, finishing none. Pick one, on the first day, and commit.

Good options (any of these works, you do not need all of them):

• MIT OCW 6.006 "Introduction to Algorithms" (free, video). Heavier than you strictly need but rigorous. Skip the lectures on RSA, NP-completeness, and computational geometry.
• Stanford "Algorithms Specialization" on Coursera by Tim Roughgarden. Four short courses, ~4 weeks part-time each, but realistically you only need parts 1 and 2.
• "Algorithms, Part I" on Coursera by Robert Sedgewick (Princeton). Java-based, which is a downside; the explanations are unusually clean.
• NeetCode's free roadmap (neetcode.io). Not a course in the academic sense, but a structured path through the problems that maps very well to the lessons in this sub-path.

If you want one recommendation: NeetCode roadmap. It's Python-based, exactly the right depth, and the problems are the LeetCode problems you'd be doing in step 2 anyway. Course and practice are merged.

Whichever you pick, do these things and only these things:

• Watch/read in order. Don't jump around.
• Implement every algorithm once, by hand, in Python.
• Skim the parts I list below as "skippable."
• Stop when you finish the course. Don't add a second one.

Step 2: ~50 LeetCode problems, curated

After the course, you need fluency, which only comes from solving problems against a timer. ~50 is the sweet spot: enough to build pattern recognition, not so many that you're grinding endlessly.

The list, organised by lesson

Arrays, sets, dicts (CS2):

• Two Sum (easy)
• Contains Duplicate (easy)
• Group Anagrams (medium)
• Top K Frequent Elements (medium)
• Longest Consecutive Sequence (medium)

Stacks and queues (CS2):

• Valid Parentheses (easy)
• Min Stack (medium)
• Implement Queue using Stacks (easy)
• Daily Temperatures (medium)

Heaps (CS2):

• Kth Largest Element in an Array (medium)
• Find Median from Data Stream (hard, do this once for completeness)
• Task Scheduler (medium)

Trees and recursion (CS2, CS3):

• Invert Binary Tree (easy)
• Maximum Depth of Binary Tree (easy)
• Same Tree (easy)
• Binary Tree Level Order Traversal (medium, BFS!)
• Lowest Common Ancestor (medium)

Graphs and traversal (CS3, the most scraping-relevant):

• Number of Islands (medium, DFS)
• Clone Graph (medium)
• Course Schedule (medium, cycle detection)
• Pacific Atlantic Water Flow (medium)
• Rotting Oranges (medium, BFS)
• Word Ladder (hard, BFS)

Binary search (CS3):

• Binary Search (easy)
• Search a 2D Matrix (medium)
• Find Minimum in Rotated Sorted Array (medium)

Sliding window and two pointers (CS3, useful for parsing streams):

• Best Time to Buy and Sell Stock (easy)
• Longest Substring Without Repeating Characters (medium)
• 3Sum (medium)
• Container With Most Water (medium)

Linked lists (CS2, mostly interview surface):

• Reverse Linked List (easy)
• Linked List Cycle (easy, Floyd's algorithm)
• Merge Two Sorted Lists (easy)

Light DP (CS3, the bare minimum):

• Climbing Stairs (easy)
• House Robber (medium)
• Coin Change (medium)
• Longest Common Subsequence (medium)

That's ~35 problems. Add 15 more from the "Top Interview 150" filter on LeetCode, picking ones you find hard but feel within reach. Total: ~50. Stop.

How to solve them

For each problem:

1. Read the problem, then try for 20 minutes without help.
2. If stuck, look at the hint, not the solution. Try again.
3. If still stuck after 40 minutes total, read the solution, close it, and re-implement from memory.
4. Note the pattern (BFS, sliding window, hash map dedup, etc.) so the next problem in the same family is faster.

Pace: 2 to 4 problems per evening session. 50 problems is 12 to 25 sessions. Six weeks at three sessions per week.

What to skip

The bigger the CS prep list, the less of it you need. Skip:

• Advanced graph algorithms. Dijkstra, A*, Bellman-Ford, max-flow, MST. Real scraping does not use these. If a job interview wants Dijkstra, you can learn it in a focused half-day later.
• Heavy dynamic programming. Anything beyond the four DP problems above. DP is its own discipline; the marginal value for scrapers after the basics is zero.
• Segment trees, Fenwick trees, tries, disjoint set union. Cool, mostly unused in scraping. Tries occasionally for autocomplete-style problems; learn one if interested, otherwise skip.
• Bit manipulation tricks. Useful for embedded work, near-useless for scraping. The exception is bitset-based dedup, which you'll learn on the job if you need it.
• System design rounds. A separate skill, not part of DSA. Worth its own prep, but not in this sub-path.
• Concurrent and parallel algorithms. Important for production scrapers, covered in the Production sub-path, not here.

If a course or list pushes you into these, skip those modules. They are not "more advanced versions of what you need"; they are different territory entirely.

When you're done

You're done when:

1. You can read Scrapy's scheduler.py or dupefilter.py without confusion. Open them now; if they look like gibberish, you're not done. Open them at the end of the sub-path; they should read like English.
2. A LeetCode medium graph problem feels routine. Number of Islands, Clone Graph, Course Schedule, all in 15-20 minutes without hints.
3. You can sketch a BFS crawler on a whiteboard in one minute without looking anything up. This is the test most scraping interviewers actually run.

If all three are true, stop. Go back to shipping scrapers. The Production sub-path's lessons on dedup at scale, distributed crawling, and queue design will land much better now that you have this foundation.

Re-reading vs forgetting

CS knowledge half-lifes. A year after finishing this sub-path you'll have forgotten the implementation details of binary search. That's fine. The point isn't to memorise; it's to keep the vocabulary live so you reach for the right tool. When you need binary search again, you'll re-derive it in 10 minutes. That's a thousand times faster than someone who's never seen it.

Re-read CS2 and CS4 once a year. They're short and they're the parts that actually bite. The rest you can refresh just-in-time.

Where to practice

• NeetCode.io for problem grouping by pattern. Free, well-organised, Python-friendly.
• LeetCode "Top Interview 150" for breadth.
• Cracking the Coding Interview (Gayle Laakmann McDowell) as a reference; don't read cover-to-cover, but use it to look up specific topics.
• Skiena's "The Algorithm Design Manual" if you want one book on the shelf that goes deeper than this sub-path. Optional.

That's the sub-path. Five lessons, ~4 to 6 weeks, and you have the CS shape you need. Next, if you're going further: the Go sub-path is the other optional appendix.