DSA — Algorithms & Core Patterns

Modern coding interviews don't test algorithms in isolation — they test pattern recognition. Given a new problem, can you spot that it's a sliding-window question, or a monotonic-stack question, or a binary-search-on-the-answer question? That's the skill FAANG, fintech, and product-company interviews actually probe, and it's exactly what this course builds. 1,700+ practice MCQs across 13 algorithmic-pattern topics — sorting, binary search, recursion and backtracking, divide-and-conquer, greedy, two-pointer, sliding window, prefix sums, BFS and DFS, monotonic stack and deque, bit manipulation — with instant explanations on every wrong answer.

The course is Part 2 of Abekus's four-course DSA Mastery series. Part 1 covers the data structures themselves (DSA: Data Structures Fundamentals). This course covers the algorithmic patterns that operate on those structures. Take Part 1 first if your data-structure mental model isn't solid; come straight here if it is. Either way, you practice one MCQ at a time, see exactly why your answer was wrong (or why the right answer worked), and move on. Thirteen topics, three to four subtopics each, every question vetted and mapped — that's the structure.

• Format — 1,700+ free practice MCQs across 13 top-level topics, with instant explanations after every wrong answer.
• Duration — about 19 hours of focused practice; most learners spread it over 3–5 weeks.
• Level — intermediate to advanced. Assumes comfort with arrays, hashmaps, linked lists, stacks/queues, trees, heaps, and graphs from Part 1.
• Cost — completely free, including the verifiable completion certificate.
• Audience — coding-interview candidates targeting FAANG and product companies, GATE CSE aspirants, competitive programmers, working developers preparing for senior interviews.
• Prerequisite — finish DSA: Data Structures Fundamentals first, or have equivalent comfort with the underlying data structures.

Four audiences. First, coding-interview candidates targeting FAANG, product companies, fintech, and high-growth startups — pattern recognition is the single most-tested skill in 60–90 minute interview rounds. Second, GATE CSE aspirants — the Algorithms section is built around the same paradigms (divide-and-conquer, greedy, BFS/DFS, binary search) plus complexity analysis. Third, competitive programmers (Codeforces, CodeChef, LeetCode contests) who need fluency on sliding-window, two-pointer, and monotonic-stack tricks. Fourth, working developers preparing for senior interviews where pattern-selection under time pressure separates strong candidates from average ones. The course assumes you've already worked through the data structures themselves; if not, do Part 1 first.

The curriculum is organised into three layers that map directly to the 13 topics. You can take them in order or jump to whichever layer matches your current level.

Foundations

• Introduction to Algorithms & Patterns — what an algorithmic pattern is, why pattern selection beats memorisation, and the analysis framework (correctness, complexity, space-time trade-off) used across the rest of the course.
• Sorting Algorithms — basic and advanced comparison sorts (insertion, merge, quick, heap), non-comparison sorts (counting, radix, bucket), and sort properties (stability, in-place, adaptive).
• Binary Search & Search Variants — search basics, lower-bound and upper-bound binary search variants, and binary search on the answer (the pattern that catches most candidates off guard in interviews).

Core patterns

• Recursion & Backtracking — recursion fundamentals (base case, recursive case, stack frames), the backtracking pattern for constraint-satisfaction (N-Queens, subsets, permutations, Sudoku), and recursion optimisation including memoisation hand-off to DP.
• Divide & Conquer — the paradigm, canonical examples (merge sort, quicksort, closest-pair, Strassen's matrix multiplication intro), and analysis via the master theorem.
• Greedy Algorithms — the greedy paradigm, classic greedy problems (interval scheduling, fractional knapsack, Huffman coding intro, MST core ideas), and recognising greedy failures so you know when to reach for DP instead.
• Two Pointers & Fast/Slow Pointers — the two-pointer pattern on sorted arrays, the fast/slow pointer pattern on linked lists (Floyd's cycle detection), and common problems that map to either.
• Sliding Window — fixed-size window problems, variable-size window problems (longest substring without repeating characters and friends), and windows with auxiliary structures (hashmap for character counts, deque for max/min).
• Prefix Sums & Difference Arrays — 1D and 2D prefix sums for range-sum queries in O(1) per query, and difference arrays for O(1) range updates with O(n) materialisation at the end.

Advanced patterns

• BFS & DFS Fundamentals — the BFS pattern (level-order traversal, shortest-path-in-unweighted-graph), the DFS pattern (preorder/postorder, connected components, topological sort intro), and the BFS/DFS variants that come up across grid and graph problems.
• Monotonic Stack & Deque — the monotonic stack pattern (next-greater-element, largest-rectangle-in-histogram), the monotonic deque pattern (sliding-window max/min), and classic monotonic problems that interviewers reach for to filter strong candidates.
• Bit Manipulation — bit operation basics (AND, OR, XOR, NOT, shifts), common bit tricks (count set bits, lowest set bit, swap without temp, power-of-two check), and bitmask patterns including the bitmask-DP setup that Part 3 builds on.
• Algorithms & Patterns Mastery — mixed-topic questions that don't tell you the pattern up front. Pattern comparisons, when-to-use-what selection, and applied scenarios. The wrap-up that simulates real interview pressure.

Both, in roughly equal measure. Data structures determine the containers your algorithm runs on; algorithms determine what you do with those containers. A real interview question — "given a stream of integers, return the kth largest in O(log n) per insert" — needs both data-structure selection (a min-heap of size k) and an algorithmic pattern (maintain the heap as you process the stream). You can't do either in isolation. That's why this course is positioned as Part 2 of a four-part series, not as a standalone — the data structures from Part 1 are the building blocks the algorithms here operate on. After this course, Part 3 (Dynamic Programming & Graphs) and Part 4 (Interview Mastery) build the last layer of interview readiness.

Recognition first, implementation second. Reading a tutorial on sliding window tells you what a sliding-window solution looks like; solving 30 sliding-window MCQs in an hour teaches you to recognise when a new problem you've never seen is a sliding-window problem. The Abekus format works because every wrong answer triggers an explanation that names the pattern, the trigger conditions ("this is a sliding-window problem because the constraint asks for the longest/shortest subarray with property X"), and the trap.

Pair the MCQ practice with implementing the templates yourself. After finishing the Sliding Window topic, write the variable-size sliding-window template once in your preferred language and keep it in a snippets file. Same for Two Pointers, Backtracking, Monotonic Stack. After 13 topics you'll have a 13-template snippets file that covers about 70 percent of the interview problems you'll see.

You see one question at a time. It's usually a problem statement plus 4 candidate approaches, or a code snippet plus a question on its complexity or behaviour. You pick the right pattern, predict the complexity, or identify the bug. If you're right, you move on. If you're wrong, you get a written explanation — what the right pattern is, which trigger conditions name it, what the complexity actually is. The platform tracks your accuracy per topic so you know where the gaps are. An AI guide is available alongside if you want a question explained differently.

The honest math: 1,725 MCQs at roughly 40 seconds per question works out to about 1,150 minutes — call it 19 hours. That's actual focused time. Most learners spread it across 3 to 5 weeks at 60–90 minutes per day. The foundations layer (Introduction, Sorting, Binary Search) takes about 4 hours; the core-patterns layer (Recursion & Backtracking, Divide & Conquer, Greedy, Two Pointers, Sliding Window, Prefix Sums) takes 9 hours, with Recursion & Backtracking and Sliding Window being the densest topics; the advanced layer (BFS & DFS, Monotonic Stack & Deque, Bit Manipulation, Mastery) closes in another 6 hours.

Faster learners who've solved 200+ LeetCode problems can finish in 2 weeks by skimming the foundations and spending real time on the advanced patterns. Total beginners to algorithmic problem-solving take 5–6 weeks because Recursion & Backtracking and Monotonic Stack need extra time. Either timeline is fine — what matters is finishing all 13 topics.

This is Part 2 of Abekus's four-course DSA Mastery series. The natural sequence is: DSA: Data Structures Fundamentals → this course → DSA — Dynamic Programming & Graphs (memoisation, tabulation, shortest paths, MST, topological sort) → DSA — Interview Mastery (mixed mock-style problems that simulate real coding rounds).

For language pairing, take this course alongside Python Fundamentals if your Python basics need work — most modern interview prep happens in Python and Abekus's question explanations assume language familiarity. The completion certificate from this course is publicly verifiable and can be added to your resume or LinkedIn profile.