KSB Mapping¶

How this entire module maps to the Level 6 Data Scientist (Integrated Degree) Apprenticeship Standard ST0585.

Knowledge (K)¶

KSB	Standard Description	M9 Relevance
K1	Context of Data Science in relation to computer science, statistics and software engineering	Understanding where ML sits within the broader Data Science discipline
K4.1	Statistical and mathematical models and methods	Statistical foundations underpinning ML: distributions, probability, linear algebra
K4.2	Advanced and predictive analytics, ML and AI techniques, simulations, optimisation, automation	Core of the module: supervised/unsupervised learning, ensemble methods, model selection, hyperparameter tuning
K4.4	Computing and organisational resource constraints and trade-offs in selecting models, algorithms and tools	Model complexity vs interpretability, overfitting vs underfitting, computational cost
K5.1	Sources of data including files, operational systems, databases, web services, open data	Loading and exploring data from multiple sources; understanding data provenance
K5.2	Data formats, structures and data delivery methods including unstructured data	Handling different data types, feature types, encoding strategies
K5.3	Common patterns in real-world data	Missing data patterns, class imbalance, multicollinearity, outliers, skewness

KSB	Standard Description	M9 Relevance
S1	Identify and clarify problems, reformulate into DS problems, apply scientific methods, hypothesis testing	Framing business challenges as ML problems; hypothesis-driven approach
S2	Data engineering: create and handle datasets, source, explore, profile, pipeline, combine, transform, store	Data preparation pipeline: loading, cleaning, transforming, feature engineering
S3	Use programming languages and tools for data manipulation, analysis, visualisation, and integration	Python/scikit-learn/pandas implementation; reproducible notebooks
S4	Use analysis and models to inform organisational outcomes; statistical analysis, feature selection, ML	Feature selection, model building, validation, and comparison
S5	Implement data solutions using software engineering architectures; evaluate deployment; assess value and ROI	Linking ML outcomes to organisational goals; deployment considerations
S6	Communicate and disseminate outputs through creative storytelling; visualise data; make recommendations	Articulating data-driven conclusions; defending recommendations to decision makers

KSB	Standard Description	M9 Relevance
B1	Inquisitive approach: curiosity, tenacity, creativity	Exploring multiple algorithms, trying different approaches, feature engineering creativity
B3	Adaptability and pragmatism when responding to varied tasks and real-world constraints	Working with imperfect real-world data; adapting approaches when models underperform
B4	Consideration of problems in context of organisation goals	Linking ML outcomes to business objectives; framing technical results for stakeholders
B5	Impartial, scientific, hypothesis-driven approach; integrity in presenting data and conclusions	Honest reporting of model limitations; avoiding data leakage
B6	Commitment to keeping up to date and maintaining personal development	Engaging with current ML research; awareness of emerging techniques

KSB	Description	How This Addresses It
K1	Context of Data Science	Understanding where ML sits within the broader discipline
S3	Programming languages and tools	Setting up the development environment and dependencies
B6	Commitment to keeping up to date	Engaging with current ML resources and research