Tier 4 · Specialist pathway · Draft

Data Scientist
Career Pathway

Six coordinated courses covering the complete 24-chapter syllabus from analytical foundations through production systems and professional mastery.

Curriculum blueprintUnpublished · Unpriced · Founder-controlled
Inspect Course 01 content and simulation lab →
06coordinated courses
24chapters
05learning levels
100%chapter coverage

Your six-course sequence

Courses in this pathway

Courses 01 and 02 have learning content and simulation labs available for inspection. The remaining courses show their planned position and production status.

  1. 01
    Levels 1–2 · 4 chapters

    Data Science Foundations

    Problem framing, Python, mathematics and probability for reliable analytical work.

    Open course
  2. 02
    Levels 1–2–3 · 6 chapters

    SQL, Statistics & Analytical Investigation

    Acquire, clean, query, explore and test data to produce defensible evidence.

    Open course
  3. 03
    Levels 2–3–4 · 6 chapters

    Applied Machine Learning

    Build, evaluate and interpret supervised, unsupervised and forecasting systems.

    Content queued
  4. 04
    Levels 3–4–5 · 2 chapters

    Advanced AI & Specialized Modeling

    Apply deep learning, language models and generative AI with rigorous evaluation.

    Content queued
  5. 05
    Levels 3–4–5 · 3 chapters

    Production Data Science, MLOps & Governance

    Build dependable pipelines, deploy models, monitor risk and govern the lifecycle.

    Content queued
  6. 06
    Levels 4–5 · 3 chapters

    Professional Data Science Capstone

    Lead a domain-adapted data product from problem discovery through executive defense.

    Content queued

Progression model

Five levels of increasing independence

  1. L1Orientation
  2. L2Guided application
  3. L3Independent practice
  4. L4Complex scenarios
  5. L5Mastery assessment

Complete curriculum

Six courses, one professional outcome

Open a course to inspect every chapter, topic and required piece of competency evidence.

01

Levels 1–2 · 4 chapters

Data Science Foundations

Problem framing, Python, mathematics and probability for reliable analytical work.
Chapter 01Level 1 · Orientation

The Data Science Profession

Topics and subtopics

  • Data science lifecycle
  • Analytics versus data science
  • Role boundaries
  • Descriptive, diagnostic, predictive and prescriptive analytics
  • Business questions
  • Success metrics
  • Project failure modes
  • Reproducibility
  • Ethical responsibility
Competency
problem framing
Required evidence
A decision-focused project brief
Chapter 02Level 1 · Orientation

Python for Data Science

Topics and subtopics

  • Python syntax
  • Collections
  • Control flow
  • Functions and modules
  • Exceptions
  • Files
  • NumPy
  • Pandas
  • Notebooks and scripts
  • Environments
  • Testing
  • Debugging
  • Git
Competency
python analysis
Required evidence
A tested reproducible analysis package
Chapter 03Level 1 · Orientation

Mathematics for Data Science

Topics and subtopics

  • Functions
  • Vectors
  • Matrices
  • Linear transformations
  • Distance and similarity
  • Derivatives
  • Gradients
  • Optimization
  • Gradient descent
  • Logarithms
  • Numerical stability
Competency
mathematical reasoning
Required evidence
An explained mathematical model of an analytical problem
Chapter 04Level 1 · Orientation

Probability Foundations

Topics and subtopics

  • Events
  • Conditional probability
  • Bayes theorem
  • Random variables
  • Expectation
  • Variance
  • Common distributions
  • Joint distributions
  • Sampling distributions
  • Central Limit Theorem
  • Simulation
Competency
probabilistic reasoning
Required evidence
A calibrated uncertainty analysis
02

Levels 1–2–3 · 6 chapters

SQL, Statistics & Analytical Investigation

Acquire, clean, query, explore and test data to produce defensible evidence.
Chapter 05Level 2 · Guided application

SQL and Relational Data

Topics and subtopics

  • Relational models
  • Keys
  • Nulls
  • Filtering
  • Aggregation
  • Joins
  • Subqueries
  • CTEs
  • Window functions
  • Dates and text
  • Validation
  • Performance fundamentals
Competency
sql analysis
Required evidence
A validated analytical query set
Chapter 06Level 2 · Guided application

Data Collection and Integration

Topics and subtopics

  • Structured and unstructured data
  • CSV JSON and Parquet
  • APIs
  • Pagination
  • Rate limits
  • Survey data
  • Event data
  • Schemas
  • Source reliability
  • Lineage
  • Sensitive data
  • Data contracts
  • Object storage
Competency
data acquisition
Required evidence
A source-to-analysis data lineage record
Chapter 07Level 2 · Guided application

Data Cleaning and Preparation

Topics and subtopics

  • Profiling
  • Missingness
  • Duplicates
  • Invalid values
  • Outliers
  • Types
  • Text normalization
  • Dates and time zones
  • Encoding
  • Scaling
  • Reconciliation
  • Leakage
  • Class imbalance
  • Validation tests
Competency
data quality
Required evidence
A reproducible preprocessing pipeline with tests
Chapter 08Level 2 · Guided application

Exploratory Analysis and Visualization

Topics and subtopics

  • Distributions
  • Group comparisons
  • Correlation
  • Multivariate analysis
  • Outliers
  • Segmentation
  • Time patterns
  • Confounding
  • Simpson's paradox
  • Chart selection
  • Visualization tools
  • Accessibility
  • Data storytelling
Competency
exploratory analysis
Required evidence
An accessible evidence-backed analytical report
Chapter 09Level 2 · Guided application

Statistical Inference

Topics and subtopics

  • Sampling
  • Bias
  • Estimation
  • Confidence intervals
  • Hypothesis tests
  • Error types
  • Power
  • Effect size
  • Non-parametric tests
  • Multiple comparisons
  • Bootstrap
  • Practical significance
Competency
statistical inference
Required evidence
A defensible inference memo
Chapter 10Level 2 · Guided application

Experimentation and Causal Thinking

Topics and subtopics

  • Randomization
  • Controls
  • Sample size
  • A/B tests
  • Guardrails
  • Sequential testing
  • Network effects
  • Quasi-experiments
  • Difference-in-differences
  • Matching
  • Causal diagrams
  • Confounding
Competency
experimentation
Required evidence
An experiment design and decision recommendation
03

Levels 2–3–4 · 6 chapters

Applied Machine Learning

Build, evaluate and interpret supervised, unsupervised and forecasting systems.
Chapter 11Level 3 · Independent practice

Machine Learning Workflow

Topics and subtopics

  • Problem framing
  • Targets
  • Baselines
  • Data splitting
  • Cross-validation
  • Leakage
  • Features
  • Pipelines
  • Tuning
  • Evaluation
  • Error analysis
  • Reproducibility
  • Business value
Competency
ml workflow
Required evidence
A reproducible baseline-to-candidate experiment record
Chapter 12Level 3 · Independent practice

Regression

Topics and subtopics

  • Linear regression
  • Assumptions
  • Regularization
  • Nonlinearity
  • Tree regression
  • Random forests
  • Gradient boosting
  • Metrics
  • Residuals
  • Prediction intervals
  • Interpretation
  • Cost-sensitive regression
Competency
regression
Required evidence
A validated regression model and residual analysis
Chapter 13Level 3 · Independent practice

Classification

Topics and subtopics

  • Binary and multiclass tasks
  • Logistic regression
  • Trees
  • Ensembles
  • Nearest neighbours
  • SVM
  • Naive Bayes
  • Calibration
  • Confusion matrix
  • Precision and recall
  • ROC and PR curves
  • Thresholds
  • Imbalance
Competency
classification
Required evidence
A thresholded classifier supported by cost analysis
Chapter 14Level 3 · Independent practice

Unsupervised Learning

Topics and subtopics

  • Similarity
  • K-means
  • Hierarchical clustering
  • Density clustering
  • Cluster evaluation
  • Segmentation
  • PCA
  • Dimensionality reduction
  • t-SNE and UMAP
  • Association rules
  • Anomaly detection
Competency
unsupervised learning
Required evidence
A stability-tested segmentation or anomaly report
Chapter 15Level 3 · Independent practice

Time-Series Analysis and Forecasting

Topics and subtopics

  • Trend and seasonality
  • Stationarity
  • Lags
  • Autocorrelation
  • Time validation
  • Baselines
  • Exponential smoothing
  • ARIMA
  • ML forecasting
  • Hierarchies
  • Intervals
  • Intermittent demand
  • Drift
Competency
forecasting
Required evidence
A backtested forecast with uncertainty and operating recommendation
Chapter 16Level 3 · Independent practice

Model Interpretation and Decision Support

Topics and subtopics

  • Global and local explanation
  • Feature importance
  • Partial dependence
  • SHAP
  • Counterfactuals
  • Stability
  • Limitations
  • Human review
  • Thresholds
  • Cost-benefit
  • What-if analysis
  • Prescriptive analytics
Competency
model interpretation
Required evidence
A human-review decision interface and explanation audit
04

Levels 3–4–5 · 2 chapters

Advanced AI & Specialized Modeling

Apply deep learning, language models and generative AI with rigorous evaluation.
Chapter 17Level 4 · Complex scenarios

Deep Learning Foundations

Topics and subtopics

  • Neural networks
  • Tensors
  • Activations
  • Losses
  • Backpropagation
  • Optimizers
  • Regularization
  • Transfer learning
  • Embeddings
  • CNNs
  • Sequence models
  • Transformers
  • Compute trade-offs
Competency
deep learning
Required evidence
A justified deep-learning experiment with failure analysis
Chapter 18Level 4 · Complex scenarios

NLP and Generative AI

Topics and subtopics

  • Tokenization
  • TF-IDF
  • Text classification
  • Embeddings
  • Transformers
  • Foundation models
  • Structured extraction
  • RAG
  • Vector search
  • Evaluation datasets
  • Hallucination
  • Prompt injection
  • Human evaluation
  • Synthetic data
Competency
nlp generative ai
Required evidence
An evaluated source-grounded language application
05

Levels 3–4–5 · 3 chapters

Production Data Science, MLOps & Governance

Build dependable pipelines, deploy models, monitor risk and govern the lifecycle.
Chapter 19Level 4 · Complex scenarios

Data Engineering for Data Scientists

Topics and subtopics

  • Batch and streaming
  • ETL and ELT
  • Warehouses lakes and lakehouses
  • Columnar storage
  • Partitioning
  • Orchestration
  • Incremental processing
  • Data modeling
  • Feature stores
  • Versioning
  • Pipeline tests
  • Observability
  • Schema evolution
  • Recovery
Competency
data pipelines
Required evidence
A tested observable analytical data pipeline
Chapter 20Level 4 · Complex scenarios

Deployment and MLOps

Topics and subtopics

  • Maintainable project structure
  • Packaging
  • Tests
  • Model serialization
  • Batch and online inference
  • APIs
  • Containers
  • Cloud concepts
  • Experiment tracking
  • Model registry
  • CI/CD
  • Monitoring
  • Drift
  • Retraining
  • Rollback
  • Incidents
  • Cost
Competency
mlops
Required evidence
A deployable monitored model release package
Chapter 21Level 4 · Complex scenarios

Responsible Data Science and AI Governance

Topics and subtopics

  • Privacy
  • Consent
  • Security
  • Data minimization
  • De-identification
  • Bias
  • Fairness
  • Subgroup evaluation
  • Accessibility
  • Explainability
  • Model cards
  • Risk classification
  • Human oversight
  • Third-party risk
  • Audit
  • Incident reporting
  • IP
Competency
responsible data science
Required evidence
A NIST-aligned risk and governance dossier
06

Levels 4–5 · 3 chapters

Professional Data Science Capstone

Lead a domain-adapted data product from problem discovery through executive defense.
Chapter 22Level 5 · Mastery assessment

Business and Product Leadership

Topics and subtopics

  • Stakeholder discovery
  • Valuable decisions
  • Value metrics
  • Financial impact
  • Feasibility
  • Build versus buy
  • Roadmaps
  • Prioritization
  • Executive communication
  • Adoption
  • Post-launch value
  • Sunsetting
Competency
data product leadership
Required evidence
An executive data-product investment recommendation
Chapter 23Level 5 · Mastery assessment

Domain Adaptation

Topics and subtopics

  • Domain vocabulary
  • Operational workflow
  • Data limitations
  • Metrics
  • High-cost errors
  • Regulation
  • Stakeholders
  • Escalation
  • Marketing
  • Finance
  • Healthcare
  • Supply chain
  • Manufacturing
  • Sustainability
  • Public services
Competency
domain adaptation
Required evidence
A domain-specific solution reviewed for risk and operational fit
Chapter 24Level 5 · Mastery assessment

Integrated Data Science Capstone

Topics and subtopics

  • Discovery
  • Data audit
  • Reproducible pipeline
  • Baselines
  • Model comparison
  • Error analysis
  • Subgroup analysis
  • Risk review
  • Deployment design
  • Monitoring
  • Documentation
  • Technical presentation
  • Executive presentation
  • Adversarial review
  • Revision
Competency
professional mastery
Required evidence
A portfolio-ready end-to-end data product and oral defense

Simulation policy

Coverage determines quantity.

There is no fixed simulation quota. Every competency must be taught, practised and assessed with observable evidence. Course Production will add only the simulations required to prove complete syllabus coverage.

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