Intro to Algorithms & Problem Solving

Hexadigitall Academy (Hexadigitall Technologies)

www.hexadigitall.com

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Course Snapshot

Learn the basics of computational thinking, logic, and problem-solving strategies.

Welcome to Intro to Algorithms & Problem Solving! 🎓

This curriculum is designed to take you from core understanding to confident delivery through weekly applied practice, measurable outcomes, and portfolio evidence.

Each week builds progressively with practical tasks, implementation checkpoints, and reflection points so you can convert knowledge into repeatable professional performance.

Your success is our priority. Stay consistent with weekly execution, document your work, and use feedback loops to continuously improve your delivery quality.

Prerequisites

Programming language proficiency with solid grasp of data structures, algorithms, and design patterns
Version control mastery: Git workflows, code review, merge conflict resolution, and collaborative development
Testing fundamentals: unit testing, test-driven development, mocking, and test coverage analysis
Debugging and profiling skills: breakpoint debugging, performance flame graphs, and memory analysis

Learning Outcomes

Computational thinking fundamentals
Basic algorithmic patterns
Problem decomposition strategies
Simple data structures (arrays, lists)
Introduction to Big O notation

Recommended Complementary Courses

Pair this curriculum with a related foundation or advanced specialization to strengthen adjacent skill areas.
Select one systems-focused and one delivery-focused course to improve both implementation depth and execution speed.
Use complementary study tracks to broaden portfolio evidence and improve interview and project readiness.

Essential Learning Resources

Language-specific style guides, design patterns reference, and debugging tools inventory
Testing frameworks, mock libraries, and performance profiling instructions for your tech stack
Refactoring patterns, security best practices, and code quality tooling configurations

Your Learning Roadmap

Foundation

Weeks 1-3

Early Weeks: Language fundamentals, testing basics, and design patterns
Middle Weeks: Advanced architectures, performance optimization, and debugging
Late Weeks: Production systems, scalability, and cross-platform deployment

Build

Weeks 4-6

Arrays, Lists, and Basic Data Handling
Searching Techniques: Linear and Binary Search
Sorting Concepts: Bubble, Selection, and Insertion

Integration

Weeks 7-9

Functions, Reuse, and Breaking Problems into Parts
Recursion Fundamentals and Base Cases
Intro to Complexity and Big O Reasoning

Capstone

Weeks 10-12

Stacks, Queues, and Practical Data Structures
Solving Interview-Style Logic Problems
Capstone: Algorithm Walkthrough and Code Translation

Detailed Weekly Curriculum

Week 1 2 hours/week + labs

Computational Thinking and Problem Decomposition

Identify the principles of Computational Thinking and Problem Decomposition and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Computational Thinking and Problem Decomposition in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Computational Thinking and Problem Decomposition, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Computational Thinking and Problem Decomposition with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 2 2 hours/week + labs

Step-by-Step Algorithms and Pseudocode

Identify the principles of Step-by-Step Algorithms and Pseudocode and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Step-by-Step Algorithms and Pseudocode in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Step-by-Step Algorithms and Pseudocode, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Step-by-Step Algorithms and Pseudocode with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 3 2 hours/week + labs

Variables, State, and Trace Tables

Identify the principles of Variables, State, and Trace Tables and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Variables, State, and Trace Tables in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Variables, State, and Trace Tables, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Variables, State, and Trace Tables with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 4 2 hours/week + labs

Arrays, Lists, and Basic Data Handling

Identify the principles of Arrays, Lists, and Basic Data Handling and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Arrays, Lists, and Basic Data Handling in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Arrays, Lists, and Basic Data Handling, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Arrays, Lists, and Basic Data Handling with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 5 2 hours/week + labs

Searching Techniques: Linear and Binary Search

Apply the principles of Searching Techniques: Linear and Binary Search and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Searching Techniques: Linear and Binary Search in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Searching Techniques: Linear and Binary Search, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Searching Techniques: Linear and Binary Search with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 6 2 hours/week + labs

Sorting Concepts: Bubble, Selection, and Insertion

Apply the principles of Sorting Concepts: Bubble, Selection, and Insertion and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Sorting Concepts: Bubble, Selection, and Insertion in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Sorting Concepts: Bubble, Selection, and Insertion, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Sorting Concepts: Bubble, Selection, and Insertion with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 7 2 hours/week + labs

Functions, Reuse, and Breaking Problems into Parts

Apply the principles of Functions, Reuse, and Breaking Problems into Parts and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Functions, Reuse, and Breaking Problems into Parts in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Functions, Reuse, and Breaking Problems into Parts, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Functions, Reuse, and Breaking Problems into Parts with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 8 2 hours/week + labs

Recursion Fundamentals and Base Cases

Apply the principles of Recursion Fundamentals and Base Cases and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Recursion Fundamentals and Base Cases in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Recursion Fundamentals and Base Cases, then record rationale for stakeholder review.
Document a portfolio-ready engineering implementation dossier for Recursion Fundamentals and Base Cases with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 9 2 hours/week + labs

Intro to Complexity and Big O Reasoning

Analyze the principles of Intro to Complexity and Big O Reasoning and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Intro to Complexity and Big O Reasoning in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Intro to Complexity and Big O Reasoning, then record rationale for stakeholder review.
Defend a portfolio-ready engineering implementation dossier for Intro to Complexity and Big O Reasoning with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 10 2 hours/week + labs

Stacks, Queues, and Practical Data Structures

Analyze the principles of Stacks, Queues, and Practical Data Structures and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Stacks, Queues, and Practical Data Structures in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Stacks, Queues, and Practical Data Structures, then record rationale for stakeholder review.
Defend a portfolio-ready engineering implementation dossier for Stacks, Queues, and Practical Data Structures with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 11 2 hours/week + labs

Solving Interview-Style Logic Problems

Analyze the principles of Solving Interview-Style Logic Problems and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Solving Interview-Style Logic Problems in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Solving Interview-Style Logic Problems, then record rationale for stakeholder review.
Defend a portfolio-ready engineering implementation dossier for Solving Interview-Style Logic Problems with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 12 2 hours/week + labs

Capstone: Algorithm Walkthrough and Code Translation

Analyze the principles of Capstone: Algorithm Walkthrough and Code Translation and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Capstone: Algorithm Walkthrough and Code Translation in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Capstone: Algorithm Walkthrough and Code Translation, then record rationale for stakeholder review.
Defend a portfolio-ready engineering implementation dossier for Capstone: Algorithm Walkthrough and Code Translation with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Capstone Projects

Project 1: Search and Sort Logic Toolkit

Implement and compare foundational searching and sorting algorithms with correctness tests and runtime discussion.

Search and sort implementation package
Trace tables and test evidence
Complexity comparison summary

Project 2: Data Structure Problem Set

Solve practical problems using lists, stacks, queues, and modular function design, with clear debugging notes.

Problem solutions with reusable helpers
Edge-case test coverage report
Refactoring and maintainability notes

Project 3: Capstone: Algorithm Design Case Study

Deliver a full algorithm case study from decomposition and pseudocode to optimized code and stakeholder-friendly explanation.

End-to-end capstone solution artifact
Complexity and trade-off defense
Presentation-ready walkthrough package

Study Tips

Reserve two weekly deep-work sessions for code quality review, refactoring, and cross-browser/platform testing.
Maintain a technical debt ledger tracking complexity hotspots, test gaps, and performance bottlenecks requiring attention.
Run weekly code review discussions, learning from peer feedback and measuring code quality metrics (coverage, maintainability index, cyclomatic complexity).

Study Tips for Success

Protect consistent weekly practice time and complete hands-on work before moving to the next topic.
Document implementation decisions, trade-offs, and lessons learned after each weekly deliverable.
Review feedback quickly and ship an improved revision within the same week to reinforce retention.
Track measurable progress with checklists, test evidence, and milestone outcomes.

About Intro to Algorithms & Problem Solving

This curriculum is structured to build practical capability, consistent delivery discipline, and portfolio-ready outcomes in Intro to Algorithms & Problem Solving. It combines conceptual understanding with applied execution so learners can perform confidently in real project environments.