Applied Machine Learning & Data Science Curriculum

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Curriculum

Applied Machine Learning & Data Science

Structured, hands-on learning path for Applied Machine Learning & Data Science with detailed weekly outcomes and practical delivery.

Duration: 16 Weeks
Level: Advanced
Study Time: 2 hours/week + labs
School: Hexadigitall Academy
16 WeeksAdvancedProject-Based

Welcome to Applied Machine Learning & Data Science! 🎓

This curriculum for Applied Machine Learning & Data Science follows a Bloom-aligned progression from practical foundations to measurable professional outcomes, with weekly evidence, labs, and portfolio outputs matched to advanced 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

  • Python programming proficiency: libraries (NumPy, Pandas, scikit-learn), data structures, and API usage
  • Statistics and probability fundamentals: distributions, hypothesis testing, and experimental design
  • Machine learning basics: supervised learning, hyperparameter tuning, and model evaluation metrics
  • Hands-on experience with notebooks (Jupyter), experiment tracking, and model versioning systems

Essential Resources

  • Model development workflow guides, hyperparameter tuning references, and experiment tracking templates
  • Feature engineering playbooks, model evaluation metrics library, and production deployment checklists
  • Research paper repository, implementation examples, and performance benchmarking tools

Complementary Courses

LLMs & Generative AI

Master fine-tuning, prompt engineering, and RAG architecture patterns

MLOps & Model Deployment

Learn model serving, A/B testing, and continuous model improvement workflows

Production AI Systems

Deepen model monitoring, drift detection, and operational governance

Learning Roadmap

  • Early Weeks: ML fundamentals, data preparation, and baseline models
  • Middle Weeks: Advanced model techniques, experimentation, and tuning
  • Late Weeks: Production deployment, monitoring, and continuous improvement

Detailed Weekly Curriculum

Each week includes outcomes and practical lab work aligned to the curriculum structure.

Week 1

Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1) queries or transformations.
  • Publish Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 2

Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Build a release workflow for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) with automated checks, approvals, and artifact traceability.
  • Implement quality and security gates for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) and enforce fail-fast criteria.
  • Execute a staged promotion for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) and validate rollback safety under a controlled failure.
Week 3

Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 4

Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 5

Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 6

Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1)

2 hours + labs
Learning Outcomes
  • Analyze the principles of Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Evaluate Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Design trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Instrument Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) with metrics, logs, and tracing hooks aligned to service objectives.
  • Create actionable alerts for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) and test escalation paths using simulated incidents.
  • Perform root-cause analysis for a Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) failure scenario and document corrective actions.
Week 7

Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1)

2 hours + labs
Learning Outcomes
  • Evaluate the principles of Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 8

Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1)

2 hours + labs
Learning Outcomes
  • Evaluate the principles of Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1) in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1) outputs to a dashboard/report with reproducible refresh steps.
Week 9

Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2)

2 hours + labs
Learning Outcomes
  • Evaluate the principles of Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2) queries or transformations.
  • Publish Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 10

Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2)

2 hours + labs
Learning Outcomes
  • Evaluate the principles of Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Build a release workflow for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2) with automated checks, approvals, and artifact traceability.
  • Implement quality and security gates for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2) and enforce fail-fast criteria.
  • Execute a staged promotion for Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 2) and validate rollback safety under a controlled failure.
Week 11

Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2)

2 hours + labs
Learning Outcomes
  • Evaluate the principles of Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Design Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Optimize trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2), then record rationale for stakeholder review.
  • Justify a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 12

Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2)

2 hours + labs
Learning Outcomes
  • Design the principles of Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Experiment Tracking and Reproducibility (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 13

Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2)

2 hours + labs
Learning Outcomes
  • Design the principles of Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 14

Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2)

2 hours + labs
Learning Outcomes
  • Design the principles of Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Instrument Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) with metrics, logs, and tracing hooks aligned to service objectives.
  • Create actionable alerts for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) and test escalation paths using simulated incidents.
  • Perform root-cause analysis for a Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) failure scenario and document corrective actions.
Week 15

Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2)

2 hours + labs
Learning Outcomes
  • Design the principles of Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.
Week 16

Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2)

2 hours + labs
Learning Outcomes
  • Design the principles of Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2) and link them to course outcomes at advanced depth with architecture-level decision quality.
  • Optimize Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2) in a guided scenario using realistic tools, constraints, and quality gates.
  • Architect trade-offs, risks, and decision points for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2), then record rationale for stakeholder review.
  • Defend a portfolio-ready model evaluation brief for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2) with measurable success criteria and next actions.
Lab Exercise
  • Implement a working data workflow for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2) with schema/model decisions documented.
  • Run quality checks and performance tuning for Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2) queries or transformations.
  • Publish Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 2) outputs to a dashboard/report with reproducible refresh steps.

Capstone Projects

Project 1: Applied Machine Learning & Data Science Foundation Build

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

  • Implement and validate Applied Machine Learning & Data Science: ML Problem Framing and Baselines (Sprint 1).
  • Integrate Applied Machine Learning & Data Science: Feature Engineering and Data Pipelines (Sprint 1) with reusable workflow standards.
  • Publish evidence for Applied Machine Learning & Data Science: Model Training and Evaluation (Sprint 1) with test and quality artifacts.

Project 2: Applied Machine Learning & Data Science Integrated Systems Build

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

  • Build an end-to-end flow around Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 1) and Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 1).
  • Add controls, observability, and rollback paths for reliability.
  • Document architecture decisions and trade-offs tied to Applied Machine Learning & Data Science: Production Hardening and Rollback (Sprint 1).

Project 3: Applied Machine Learning & Data Science Capstone Delivery

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

  • Deliver a complete implementation centered on Applied Machine Learning & Data Science: Model Serving and API Integration (Sprint 2).
  • Validate readiness for Applied Machine Learning & Data Science: Monitoring, Drift, and Reliability (Sprint 2) using objective acceptance checks.
  • Present final defense and roadmap based on Applied Machine Learning & Data Science: Responsible AI and Governance (Sprint 2) outcomes.