Applied Machine Learning & Data Science - Course Curriculum

Hexadigitall Technologies https://hexadigitall.com

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Course Snapshot

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

16 Weeks

Advanced

Project-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 & What You Should Know

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

Recommended 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

Essential Learning 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

Your 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

Week 12 hours + labs

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

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 22 hours + labs

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

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 32 hours + labs

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

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 42 hours + labs

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

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 52 hours + labs

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

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 62 hours + labs

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

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 72 hours + labs

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

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 82 hours + labs

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

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 92 hours + labs

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

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 102 hours + labs

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

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 112 hours + labs

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

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 122 hours + labs

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

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 132 hours + labs

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

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 142 hours + labs

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

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 152 hours + labs

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

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 162 hours + labs

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

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.