BIG-O BLUE 2.0 AI ENGINEER: ALGORITHMS & INTERVIEW PATTERNS

Are you looking to master Algorithms but starting from scratch? Or perhaps you have some knowledge, but it feels fragmented and vague? When you search for “self-taught algorithms,” the overwhelming amount of resources, documentation, and programming languages can leave you confused about where to begin. If you are eager to challenge yourself in the demanding yet incredibly rewarding world of Algorithms but lack a clear roadmap, you’ve come to the right place.

Big-O Blue 2.0 AI Engineer: Algorithms & Interview Patterns is designed specifically for you. This course provides a comprehensive introduction to core algorithms, guiding you through solving problems on world-class Online Judges (Codeforces, Topcoder, SPOJ, etc.). Featuring real-world examples and exercises structured from beginner to advanced levels, we ensure every concept is visualized and easy to grasp. Lectures are demonstrated in C++, Python, and Java.

Tuition Fee: Special offer for the first 5 early registrants. For details of tuition fees, please see the attached link below.

We also have a program to support paying tuition fees many times for students. Please contact us via Email:  admin@bigocoding.com for information.

You can view the opening time, class timetable and register via this link.

IDEAL CANDIDATES

  • Are you ready for Blue 2.0?
  • Prerequisites: Proficiency in basic programming techniques (Variables, Loops, Functions, Arrays) in C++, Java, or Python is required.
  • This course is designed for:
    – Students & Developers looking to systematically rebuild their Data Structures & Algorithms (DSA) knowledge.
    – Individuals who feel “vague” about algorithms and want to learn from the ground up to gain absolute confidence.
    – Candidates preparing for technical interviews at top-tier technology companies.
  • Not sure if you’re ready? Don’t worry! Call our hotline at 0937.401.483 for a personalized consultation on our introductory paths (Green/Orange courses).

SAMPLE COURSE EXERCISES

  • Learning by Doing: Say no to rote memorization; focus on practical application.
  • English Standardization: 100% of problem statements are in English to prepare you for a global environment (Full support from instructors and AI for translation and detailed explanations).
  • Big Tech Alignment: A curated question bank sourced from actual interview prompts (Intern/Full-time) at industry giants: Google, Meta, Amazon, Microsoft…
  • Real-World Applications: Exercises are modeled after actual software features (Keyword suggestions, Ranking systems, Logistics optimization, etc.).
  • Competitive Mindset: Sharpen your brain with problems adapted from prestigious ACM-ICPC and Informatics Olympiad competitions (tailored to your current level).

TIME AND LOCATION OF THIS COURSE

  • Duration: 2 months (8 weeks)
  • Format: Online via Microsoft Teams
  • Number of students per class: 25 to 30 students maximum.
  • Each class has 1 main teacher and teaching assistants.
  • Especially, there are weekly Office Hours for students to review the lesson if they can’t keep up with the lesson progress.

WHAT MAKES THE COURSES AT BIG-O CODING DIFFERENT

1. CURRICULUM & METHODOLOGY:

A COMPREHENSIVE AI-NATIVE EDUCATIONAL ECOSYSTEM

  • Led by Experts & Big Tech Veterans: Our faculty consists of top-tier Algorithmic experts, National/International award winners with extensive high-level experience at global tech giants.

  • Exclusive “3-Tier” Support Model: Beyond the main instructor, Big-O provides a comprehensive support network:
    1. 5 Mentors per Class: Closely monitoring progress and providing line-by-line code reviews.
    2. Alumni Community: Sharing real-world interview experiences and industry insights.
    3. 24/7 AI Teaching Assistant: Seamlessly integrated into the learning workflow to resolve syntax queries and debug errors instantly—even at 2 AM.

  • International-Standard Hands-on Platforms: Students code directly on renowned auto-grading systems such as Codeforces, LeetCode, and HackerRank… building familiarity with time pressure and the requirement for absolute precision.

2. GRADUATE PROFILE:

FROM “CODER” TO “AI ENGINEER”

  • Solid Algorithmic Foundation: Confidently solve any problem, from academic challenges to real-world applications. Eliminate the fear of “Time Limit Exceeded” errors or inefficient code.

  • Contest Ready: Fully equipped to compete in prestigious algorithmic challenges hosted by industry titans: Samsung Challenge, Meta Hacker Cup, Google Code Jam, or academic competitions such as ACM-ICPC and Informatics Olympiads.

  • Interview Ready for Big Tech: Gain the comprehensive knowledge and high-level problem-solving skills needed to conquer the most rigorous Coding Interviews at companies like Shopee, TikTok, Grab, WorldQuant, and Google.

  • AI-Native Mindset (NEW): Beyond just coding, you will master the art of AI orchestration to boost your productivity by 2-3 times. You graduate as an engineer who leverages cutting-edge technology, not just a mere “code monkey.”

BLUE COURSE SYLLABUS

  • The Problem: Why does your code return the correct result but still get rejected (Time Limit Exceeded)? How can you determine if your code is efficient without having to click "Run"?
  • The Core: Master the art of measuring performance using Big-O Notation (O(1), O(N), O(N^2)). Cultivate an optimization-first mindset from your very first lines of code.
  • AI-Native: Leverage AI to analyze code complexity and explain abstract algorithmic concepts in simple, intuitive terms.
  • The Challenge: Sorting is a fundamental problem, but how do you perform custom sorting to meet specific requirements? How do you handle complex tasks like merging meeting schedules or detecting overlapping time intervals?
  • The Core: Master sorting algorithms and Interval processing techniques. Learn to implement custom Comparators in C++, Java, and Python.
  • Outcome: Gain the confidence to solve scheduling problems and advanced data statistics tasks efficiently.
  • The Challenge: How can you calculate total revenue from Day X to Day Y in an instant (O(1)) without using a single loop?
  • The Core: The Prefix Sum technique—a "secret weapon" to reduce complexity from O(N) to O(1). Expand your expertise with 2D Prefix Sums and Difference Array techniques.
  • Outcome: Master lightning-fast Range Sum Queries, providing a solid foundation for advanced optimization problems.
  • The Challenge: How do you find a substring that meets specific criteria or a pair of numbers with a sum of K without relying on nested loops (O(N^2))?
  • The Core: Master the Two Pointers technique for efficient data convergence and Sliding Window for seamless data scanning. Transform sluggish algorithms into high-speed solutions (O(N)).
  • Outcome: Smoothly solve complex problems involving strings and contiguous subarrays.
  • The Challenge: How do you detect a cycle in a Linked List? How can you find the middle element of a list in just a single pass?
  • The Core: Master Floyd's Cycle-Finding Algorithm (The Tortoise and the Hare). Learn the dual-pointer technique with varying speeds to tackle specialized Linked List problems.
  • Outcome: Gain mastery over Linked List manipulations—a high-frequency topic in Amazon and Google technical interviews.
  • The Challenge: How do you validate balanced parentheses ((()))? How can you find the Next Greater Element in a single pass (O(N))?
  • The Core: Deep dive into LIFO (Last-In-First-Out) and FIFO (First-In-First-Out) mechanisms. Master the Monotonic Stack technique to solve proximity-based search problems efficiently.
  • AI-Native: Utilize AI to generate edge test cases (e.g., empty stack, stack overflow) to effectively debug and harden your code.
  • The Challenge: Linear search (O(N)) is too sluggish for massive datasets (e.g., 10^9 elements). Is there a faster way to pinpoint your target?
  • The Core: The "Divide and Conquer" mindset. Beyond searching in arrays, master Binary Search on Answer—the definitive technique that separates pro candidates from newbies.
  • Outcome: Search in O(log N) complexity, processing colossal amounts of data in the blink of an eye.

Midterm contest of the course.

  • The Challenge: How do you find your way through a maze? How does information go viral across a social network?
  • The Core: Master the two classic graph traversal algorithms: BFS (Breadth-First Search for shortest paths) and DFS (Depth-First Search for pathfinding and connectivity).
  • AI-Native: Leverage AI to Visualize the node traversal order, allowing your brain to literally "see" the algorithm in motion.
  • The Challenge: How do you store data so that searching, adding, and deleting are all lightning-fast? When should you choose a Map over a Set?
  • The Core: Explore the structure of Binary Search Trees (BST) and the sheer power of Hash Maps (O(1) retrieval). Solve practical problems involving frequency counting and rapid lookups.
  • Outcome: Understand the internal implementation of standard libraries (C++ STL map/set, Java HashMap, Python dict) to utilize them with maximum efficiency.
  • The Challenge: How can you efficiently retrieve the maximum or minimum element from a dynamic dataset without having to re-sort from scratch every time?
  • The Core: Explore the Heap structure and Priority Queue applications. Solve high-frequency problems like "Top K Elements" or CPU task scheduling.
  • Outcome: Master the fundamental data structure required for Dijkstra’s Algorithm and complex stream processing.
  • The Challenge: How does Google Maps calculate the shortest path from your home to school on a map with complex traffic conditions (weighted edges)?
  • The Core: Dijkstra's Algorithm integrated with a Heap/Priority Queue. Master shortest-path discovery on graphs with non-negative weights.
  • Outcome: Successfully implement the world’s most famous routing algorithm from scratch.
  • The Challenge: How do you determine if two people on a social network have a common friend or belong to the same community? How can you merge two separate groups in the fastest way possible?
  • The Core: Disjoint Set Union (DSU) / Union-Find data structure with two key optimization techniques: Path Compression and Union by Rank.
  • Outcome: Effectively solve problems regarding connectivity and cycle detection in graphs with near-constant time complexity.
  • The Challenge: How does Google's "Auto-complete" feature work? How can you store millions of dictionary words while maintaining maximum memory efficiency?
  • The Core: Master the Trie (Prefix Tree) data structure, specifically designed for high-performance string processing and retrieval.
  • Outcome: Build professional features such as digital dictionaries, keyword suggestions, and lightning-fast prefix filtering.
  • Activity: Step away from the keyboard and enter a "battle of wits." Experience a Simulated Coding Interview modeled after the actual processes at Big Tech companies.
  • AI-Native: Engage with AI acting as a tough interviewer. It will pose follow-up questions, challenge your logic, and grade your communication and technical explanation skills.
  • Goal: Build mental toughness, master strategic communication to "buy time" effectively, and learn to present your solutions with absolute conviction.

The final contest.