Hexadigitall Technologies logo
Hexadigitall Technologies https://hexadigitall.com
QR code to the course page
Scan to open the course page and view enrollment options.

Course Snapshot

Structured, hands-on learning path for Data Structures & Algorithms Fundamentals with detailed weekly outcomes and practical delivery.

12 Weeks
Beginner
Project-Based
Course QR Code

Data Structures & Algorithms Fundamentals

Professional curriculum aligned to practical delivery, portfolio quality, and implementation confidence.

Duration: 12 Weeks
Level: Beginner
Study Time: 2 hours/week + labs
School: Hexadigitall Academy

Welcome to Data Structures & Algorithms Fundamentals! 🎓

This curriculum for Data Structures & Algorithms Fundamentals follows a Bloom-aligned progression from core competencies to integrated professional delivery, with weekly evidence, labs, and portfolio outputs matched to beginner expectations.

Each week advances from comprehension and application toward evaluation and creation, ensuring progressive learning and capstone readiness.

Your success is our priority. By the end, you will produce portfolio-ready artifacts and confidently explain your technical decisions. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality. You will graduate with a professionally curated portfolio that demonstrates scope, depth, and delivery quality.

Prerequisites & What You Should Know

  • 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

Recommended Complementary Courses

Web Development (Frontend/Backend)

Specialize in modern frameworks, API design, and full-stack architecture

System Design & Architecture

Master scalability patterns, distributed systems, and reliability engineering

DevOps & Quality Assurance

Learn CI/CD integration, automated testing, and production monitoring

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

  • Early Weeks: Complexity analysis and core data structure patterns
  • Middle Weeks: Graph/tree reasoning, dynamic programming, and optimization patterns
  • Late Weeks: Interview simulation, speed/accuracy refinement, and portfolio-ready solution writeups

Detailed Weekly Curriculum

Week 12 hours + labs
Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 1)
  • Identify the principles of Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Explain Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 22 hours + labs
Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 1)
  • Identify the principles of Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Explain Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 32 hours + labs
Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 1)
  • Identify the principles of Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Explain Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 42 hours + labs
Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 1)
  • Identify the principles of Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Explain Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Apply trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 52 hours + labs
Data Structures & Algorithms Fundamentals: Dynamic Programming and Memoization (Sprint 1)
  • Apply the principles of Data Structures & Algorithms Fundamentals: Reporting and Communication Excellence (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Analyze Data Structures & Algorithms Fundamentals: Reporting and Communication Excellence (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Reporting and Communication Excellence (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Reporting and Communication Excellence (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Dynamic Programming and Memoization (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Dynamic Programming and Memoization (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Dynamic Programming and Memoization (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 62 hours + labs
Data Structures & Algorithms Fundamentals: Greedy Algorithms and Backtracking (Sprint 1)
  • Apply the principles of Data Structures & Algorithms Fundamentals: Risk and Governance Controls (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Analyze Data Structures & Algorithms Fundamentals: Risk and Governance Controls (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Risk and Governance Controls (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Risk and Governance Controls (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Greedy Algorithms and Backtracking (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Greedy Algorithms and Backtracking (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Greedy Algorithms and Backtracking (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 72 hours + labs
Data Structures & Algorithms Fundamentals: Binary Search and Two-Pointer Methods (Sprint 1)
  • Apply the principles of Data Structures & Algorithms Fundamentals: Program Execution and Cadence (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Analyze Data Structures & Algorithms Fundamentals: Program Execution and Cadence (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Program Execution and Cadence (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Program Execution and Cadence (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Binary Search and Two-Pointer Methods (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Binary Search and Two-Pointer Methods (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Binary Search and Two-Pointer Methods (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 82 hours + labs
Data Structures & Algorithms Fundamentals: Systematic Problem Decomposition and Interview Simulation (Sprint 1)
  • Apply the principles of Data Structures & Algorithms Fundamentals: Continuous Improvement and Scale (Sprint 1) and link them to course outcomes through structured core competency milestones.
  • Analyze Data Structures & Algorithms Fundamentals: Continuous Improvement and Scale (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Evaluate trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Continuous Improvement and Scale (Sprint 1), then record rationale for stakeholder review.
  • Document a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Continuous Improvement and Scale (Sprint 1) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Systematic Problem Decomposition and Interview Simulation (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Systematic Problem Decomposition and Interview Simulation (Sprint 1) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Systematic Problem Decomposition and Interview Simulation (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 92 hours + labs
Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 2)
  • Analyze the principles of Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 2) and link them to course outcomes through structured core competency milestones.
  • Evaluate Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Domain Foundations and Problem Definition (Sprint 2) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 2) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 102 hours + labs
Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 2)
  • Analyze the principles of Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 2) and link them to course outcomes through structured core competency milestones.
  • Evaluate Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Stakeholder Discovery and Requirements (Sprint 2) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 2) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 112 hours + labs
Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 2)
  • Analyze the principles of Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 2) and link them to course outcomes through structured core competency milestones.
  • Evaluate Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Process Mapping and Workflow Design (Sprint 2) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 2) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 122 hours + labs
Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 2)
  • Analyze the principles of Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 2) and link them to course outcomes through structured core competency milestones.
  • Evaluate Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Create trade-offs, risks, and decision points for Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready engineering implementation dossier for Data Structures & Algorithms Fundamentals: Decision Frameworks and Trade-offs (Sprint 2) with measurable success criteria and next actions.

Lab Exercise

  • Implement a working data workflow for Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 2) queries or transformations.
  • Publish Data Structures & Algorithms Fundamentals: Trees, Graphs, and Traversal Strategies (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.

Capstone Projects

Project 1: Data Structures & Algorithms Fundamentals Foundation Build

Deliver a concrete foundation implementation covering the first phase of the curriculum.

  • Implement and validate Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 1).
  • Integrate Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 1) with reusable workflow standards.
  • Publish evidence for Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 1) with test and quality artifacts.

Project 2: Data Structures & Algorithms Fundamentals Integrated Systems Build

Combine mid-program competencies into a production-style integrated workflow.

  • Build an end-to-end flow around Data Structures & Algorithms Fundamentals: Dynamic Programming and Memoization (Sprint 1) and Data Structures & Algorithms Fundamentals: Greedy Algorithms and Backtracking (Sprint 1).
  • Add controls, observability, and rollback paths for reliability.
  • Document architecture decisions and trade-offs tied to Data Structures & Algorithms Fundamentals: Binary Search and Two-Pointer Methods (Sprint 1).

Project 3: Data Structures & Algorithms Fundamentals Capstone Delivery

Ship a portfolio-ready capstone with measurable outcomes and stakeholder-ready presentation.

  • Deliver a complete implementation centered on Data Structures & Algorithms Fundamentals: Algorithmic Complexity and Big-O Analysis (Sprint 2).
  • Validate readiness for Data Structures & Algorithms Fundamentals: Arrays, Strings, and Hashing Patterns (Sprint 2) using objective acceptance checks.
  • Present final defense and roadmap based on Data Structures & Algorithms Fundamentals: Linked Lists, Stacks, Queues, and Heaps (Sprint 2) outcomes.