An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The MSc in Data Engineering at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE711 | Advanced Statistical Inference, Causal Modeling & Estimation Theory | Theory | Derive maximum likelihood estimators, Bayesian posterior distributions, do-calculus and DAG causal inference in Data Engineering. |
| DTE712 | Scalable Data Engineering & Apache Spark Architecture Lab | Practical | Engineer distributed ETL pipelines using Spark DataFrames, Catalyst query optimizer and PySpark on cluster nodes. |
| DTE713 | High-Dimensional Probability, Matrix Decomposition & SVD in Data Engineering | Theory | Formulate random matrix theory, concentration inequalities, Johnson-Lindenstrauss lemma and randomized SVD. |
| DTE714 | High-Throughput Streaming Data Lab (Apache Flink & Kafka) | Practical | Implement stateful stream processing pipelines with event-time semantics, watermarks and windowing in Apache Flink. |
| DTE715 | Modern Data Lakehouse Architectures, Delta Lake & Iceberg | Theory | Evaluate ACID transactional guarantees on object storage, Parquet columnar optimization and metadata caching. |
| DTE716 | Lakehouse Engineering & Trino / DuckDB Analytics Workshop | Practical | Deploy Apache Iceberg tables, optimize query partition layouts and benchmark vectorized SQL queries in DuckDB. |
| DTE717 | Empirical Data Science Research Methodology & Statistical Rigor | Theory | Formulate rigorous hypothesis testing, false discovery rate (FDR) control and reproducible scientific workflows. |
| DTE718 | Advanced Multivariate Data Visualization & Exploratory Studio | Practical | Design interactive multidimensional dashboards using Altair, D3.js and WebGL for high-volume data streams. |
| DTE719 | Swiss & European Data Governance, Privacy Law & Lineage Standards | Theory | Audit data pipelines against Swiss Federal Data Protection Act, GDPR data subject rights and automated lineage. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE721 | Deep Learning for Complex High-Dimensional & Structured Data | Theory | Analyze deep representation learning for unstructured text, spatio-temporal graphs and multi-modal data streams in Data Engineering. |
| DTE722 | Feature Store Engineering & Real-Time Data Pipelines (Feast) | Practical | Deploy Feast feature stores with Redis online stores, Snowflake offline sinks and automated point-in-time joins. |
| DTE723 | Bayesian Data Analysis, MCMC & Probabilistic Programming | Theory | Formulate Hamiltonian Monte Carlo, No-U-Turn Samplers (NUTS) and hierarchical Bayesian models in Stan/PyMC. |
| DTE724 | Probabilistic Modeling & Stan / PyMC Computation Lab | Practical | Fit hierarchical Bayesian models to sparse noisy empirical data and diagnose convergence using R-hat metrics. |
| DTE725 | Time-Series Forecasting, State Space Models & Anomaly Detection | Theory | Deconstruct ARIMA, Kalman filters, neural basis expansion forecasting (N-BEATS) and conformal prediction bands. |
| DTE726 | Large-Scale Time-Series Forecasting & Anomaly Workshop | Practical | Deploy automated forecasting pipelines processing millions of time series with anomaly detection alerts. |
| DTE727 | Data Quality Engineering, Great Expectations & Observability | Theory | Formulate data testing methodologies, schema drift contracts and automated pipeline health monitoring. |
| DTE728 | Graph Analytics & Network Science Studio (NetworkX / Neo4j) | Practical | Execute PageRank, community detection (Louvain) and knowledge graph reasoning queries over enterprise datasets. |
| DTE729 | Applied Data Science Enterprise Practicum & Solution Design | Practical | Deliver an end-to-end data analytics and predictive solution solving an authentic industry challenge in Data Engineering. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE731 | Frontiers in Scalable Machine Learning & Distributed Vector Databases | Theory | Critically assess vector embedding search algorithms (HNSW, IVF-PQ), sparse-dense retrieval and cluster indexing. |
| DTE732 | Vector Database Engineering & Semantic Search Lab (Milvus / Qdrant) | Practical | Deploy production vector search clusters, benchmark recall vs latency tradeoffs and implement hybrid retrieval. |
| DTE733 | Chief Data Officer (CDO) Leadership, Strategy & Data Monetization | Theory | Formulate corporate data monetization strategies, data mesh operating models and executive analytics governance. |
| DTE734 | Automated Machine Learning (AutoML) & Hyperparameter Studio | Practical | Construct distributed Bayesian hyperparameter optimization pipelines using Optuna and Ray Tune. |
| DTE735 | Privacy-Enhancing Data Analytics, Differential Privacy & Synthetics | Theory | Evaluate differential privacy epsilon budgets, randomized response mechanisms and synthetic data generation. |
| DTE736 | MSc Data Engineering Thesis Proposal Colloquium & Defense | Practical | Formulate and defend a formal scientific master's research proposal specifying empirical data pipelines. |
| DTE737 | Commercializing Data Products, IP Strategy & Analytics Ventures | Theory | Structure data marketplace contracts, intellectual property rights in datasets and analytics startup funding. |
| DTE738 | Pre-Thesis Data Architecture Prototyping & Pilot Lab | Practical | Construct initial experimental data pipelines and gather preliminary statistical validation metrics for the thesis. |
| DTE739 | Industry Data Science Practicum & Real-World Dataset Validation | Practical | Validate computational models against messy, production-scale industry data under strict latency bounds. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE741 | MSc Data Engineering Dissertation: Empirical Methodology & Experimental Setup | Practical | Formulate the formal statistical experimental design, data validation protocols and benchmark suites in Data Engineering. |
| DTE742 | MSc Data Engineering Dissertation: Computational Pipeline & Algorithm Engineering | Practical | Engineer the complete data science pipeline, statistical algorithm or predictive platform in Data Engineering. |
| DTE743 | MSc Data Engineering Dissertation: Statistical Validation & Comparative Benchmarking | Practical | Execute rigorous cross-validation, ablation studies and statistical significance tests on novel algorithms. |
| DTE744 | Critical Literature Synthesis & State-of-the-Art Analysis in Data Engineering | Theory | Synthesize academic publications from KDD, SIGMOD, VLDB and ICDE conferences. |
| DTE745 | Scalability Benchmarking, Pipeline Profiling & Quality Audit | Practical | Subject the data pipeline to high-throughput stress tests, resource profiling and automated data audits. |
| DTE746 | Scientific Manuscript Preparation & Peer Review in Data Engineering | Theory | Author a publication-ready academic manuscript adhering to top-tier data science and analytics standards. |
| DTE747 | Executive Strategic Data Direction & Enterprise Advisory | Theory | Translate specialized data science research into actionable executive business roadmaps and data strategies. |
| DTE748 | Comprehensive Data Portfolio & Reproducible Pipeline Repository | Practical | Package reproducible data pipelines, Dockerized execution environments and technical documentation in Data Engineering. |
| DTE749 | MSc Data Engineering Thesis Oral Defense & Public Academic Viva Voce | Practical | Defend the novel scientific thesis and empirical data models before an academic examination committee. |
Recognized Bachelor Degree in computing, engineering, mathematics, quantitative discipline or relevant technical equivalence. English proficiency IELTS 6.5+ or equivalent.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.