Bootcamps
Coming soon · the subjects below are what they will draw on
Subjects
What it is
Search, constraint satisfaction, knowledge representation, planning, and an introduction to learning.
Where it sits
School of Data Science · Upper level
You get
The learning half in full: complete ML pipelines, model choice, evaluation and explanation.
Still to add
Classical search (A*, minimax), constraint satisfaction and logical knowledge representation.
Crunch RL — Reinforcement Learning
You get
Sequential decision-making done properly: MDPs, exploration, dynamic programming and planning with a model.
What it is
Distributed processing of datasets too large for one machine, usually on Spark or an equivalent engine. The codes span levels: DS 410 is undergraduate, COMP SCI 544 is an advanced course open to undergraduates and graduate students, and CS 6220 is graduate.
Where it sits
School of Data Science · Upper level
Crunch Spark — Big Data & Distributed Processing
You get
Why one machine stops being enough, MapReduce thinking, RDDs, DataFrames and Spark SQL, file formats chosen on purpose, performance tuning, streaming pipelines, and machine learning at scale.
What it is
Sequence alignment, genomic file formats, and computational analysis of biological data.
Where it sits
School of Data Science · Upper level
You get
Parsing and manipulating sequence data, Biopython, a small variant-calling pipeline, phylogenetic trees, a basic transcriptomic analysis, R and Bioconductor, a reproducible pipeline, and reading a Nature Methods paper without drowning.
What it is
Turning business data into decisions: descriptive statistics, modelling, and communicating findings.
Where it sits
School of Data Science · Core
Crunch Stats — Statistics & Experimentation
You get
The statistical half done carefully: describing data honestly, estimation with uncertainty, hypothesis testing without fooling yourself, regression, and A/B test design.
Still to add
Prescriptive analytics — optimisation and decision modelling — and the case work that frames which question to ask in the first place.
Crunch Excel — Spreadsheets from Zero to Expert
You get
The spreadsheet half taken seriously: formulas that hold up, lookups, wrangling messy data, summarising at scale, dashboards, and decision models.
Crunch Viz — Data Visualization
You get
The communication half: choosing the right chart, using colour honestly, and telling a data story that survives questions.
What it is
Limits, derivatives, integrals, series, and the analytic groundwork the engineering courses assume.
Where it sits
School of Data Science · Intro
Not ready yet · on the build list
What it will cover
Limits and continuity, derivatives and the chain rule, optimisation, integration, series, and partial derivatives and gradients — aimed at the calculus the machine-learning and control tracks assume.
DM us to ask for it — requests decide the order
What it is
Ingestion, batch and streaming pipelines, warehouse modelling, orchestration, and data quality.
Where it sits
School of Data Science · Upper level
Crunch Data — Data Engineering & Streaming
You get
Pipelines end to end: ingestion, batch and streaming processing, warehouse modelling, orchestration, schema evolution and the data-quality checks that keep a pipeline trustworthy.
What it is
Clustering, association rules, dimensionality reduction, and finding structure in messy data.
Where it sits
School of Data Science · Upper level
You get
Reading and cleaning real data in pandas, then the modelling and evaluation half — clustering, dimensionality reduction, model selection and honest experiment design.
Still to add
Classical association-rule mining (Apriori, FP-growth).
What it is
Encoding data visually, perception, chart selection, and building dashboards people can actually read.
Where it sits
School of Data Science · Core
Crunch Viz — Data Visualization
You get
Perception and the grammar of graphics, choosing the right chart, matplotlib in depth, statistical graphics, using colour honestly, interactive dashboards, data storytelling and mapping onto geography.
What it is
Relational modelling, normalization, SQL, transactions, indexing, and query planning.
Where it sits
School of Data Science · Core
You get
SQL as a working tool: schema design and normalisation, joins, aggregation, window functions, indexes and reading a query plan.
Still to add
Storage-engine and transaction internals — B-tree implementation, concurrency control, recovery.
Crunch Data — Data Engineering & Streaming
You get
The modelling half at warehouse scale: dimensional models, slowly changing dimensions, and schemas that survive a year of changes.
What it is
Vector spaces, matrix operations, eigenvalues, decompositions, and the math under machine learning.
Where it sits
School of Data Science · Core
Not ready yet · on the build list
What it will cover
Vectors and matrices as the machine-learning courses actually use them: linear systems, rank and span, projections and least squares, eigenvalues, SVD and PCA, worked in NumPy alongside the algebra.
DM us to ask for it — requests decide the order
What it is
Supervised and unsupervised learning, model selection, regularization, and honest evaluation.
Where it sits
School of Data Science · Upper level
You get
A complete scikit-learn pipeline from raw data to a trained model, choosing the right model for a tabular problem, a small PyTorch network end to end, SHAP explanations, leakage-free experiment design, and the model shipped behind a FastAPI endpoint.
Crunch MLOps — Production Machine Learning
You get
What happens after it works: deployment, model registries, monitoring for drift and retraining.
What it is
Stochastic models, derivative pricing, backtesting, and quantitative strategy evaluation.
Where it sits
School of Data Science · Upper level
Crunch Quant — Quantitative Modeling & Financial Analytics
You get
The quant toolkit, probability reasoning, proper time-series analysis, optimal portfolios, factor models explaining returns, simulation, derivative pricing, and measuring and stress-testing risk.
What it is
Distributions, estimation, hypothesis testing, regression, and reading a result without fooling yourself.
Where it sits
School of Data Science · Core
Crunch Stats — Statistics & Experimentation
You get
Describing data honestly, probability, the central limit theorem explained rather than quoted, estimation with uncertainty, hypothesis testing without fooling yourself, regression, A/B design, guarding against false discovery, and thinking Bayesian.