An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The BSc 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 |
|---|---|---|---|
| DTE101 | Foundations of Data Science & Empirical Investigation | Theory | Formulate statistical questioning, data collection lifecycles, experimental reproducibility and hypothesis formulation in Data Engineering. |
| DTE102 | Scientific Python & Pandas Data Wrangling Lab | Practical | Load, clean, transform and manipulate tabular datasets using Python, Pandas and NumPy vector operations. |
| DTE103 | Calculus & Linear Algebra Foundations for Data Science | Theory | Compute partial derivatives, matrix inversions, vector spaces and eigenvalues for data transformations. |
| DTE104 | Linux & Command-Line Tools for Data Processing Lab | Practical | Automate CSV manipulation, text filtering, regex parsing and pipeline piping using AWK, Sed, grep and Bash. |
| DTE105 | Relational Database Systems, Schema Design & Advanced SQL | Theory | Design normalized database schemas, understand B-tree indexing and formulate multi-table relational joins. |
| DTE106 | Advanced SQL Analytics & Window Functions Workshop | Practical | Execute complex SQL analytical queries utilizing window functions, Common Table Expressions (CTEs) and aggregations. |
| DTE107 | Data Ethics, Algorithmic Fairness & Privacy Regulations | Theory | Analyze data collection consent, demographic bias in datasets, anonymization limits and Swiss/EU privacy mandates. |
| DTE108 | Exploratory Data Analysis & Visualization Lab (Seaborn) | Practical | Generate publication-quality statistical distributions, correlation heatmaps and scatter plots in Matplotlib and Seaborn. |
| DTE109 | Technical Communication & Data Storytelling in Data Engineering | Theory | Translate statistical insights, uncertainty intervals and data visualizations into actionable executive narratives. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE201 | Probability Distributions & Inferential Statistics | Theory | Calculate confidence intervals, Central Limit Theorem bounds, Student's t-tests, ANOVA and z-score transformations. |
| DTE202 | Statistical Hypothesis Testing & A/B Testing Lab | Practical | Design, execute and evaluate two-sample t-tests, chi-square independence tests and statistical power calculations. |
| DTE203 | Object-Oriented Programming for Data Pipelines Lab | Practical | Write modular, object-oriented data ingestion classes, custom exception handlers and logging pipelines in Python. |
| DTE204 | NoSQL Databases & Semi-Structured Data Processing (JSON / Parquet) | Theory | Compare document datastores (MongoDB), columnar formats (Apache Parquet) and key-value stores for analytics. |
| DTE205 | Machine Learning Foundations: Supervised Regression & Classification | Theory | Derive ordinary least squares (OLS) regression, ridge/lasso regularization, logistic regression and decision trees. |
| DTE206 | Scikit-Learn Predictive Modeling & Feature Scaling Lab | Practical | Train, evaluate and tune supervised machine learning models with k-fold cross-validation in Scikit-Learn. |
| DTE207 | Data Quality, Imputation & Feature Engineering Principles | Theory | Apply missing data imputation strategies, categorical encoding, polynomial features and outlier detection methods. |
| DTE208 | Interactive Dashboard Engineering Studio (PowerBI / Tableau) | Practical | Build interactive corporate business intelligence dashboards with dynamic filters, DAX measures and drill-downs. |
| DTE209 | Applied Data Engineering First-Year Practicum: End-to-End Analytics | Practical | Deliver an end-to-end analytical study from raw data acquisition through statistical modeling and presentation. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE301 | Advanced Machine Learning: Ensemble Methods & Gradient Boosting | Theory | Deconstruct bagging, random forests, AdaBoost, XGBoost and LightGBM gradient boosted decision trees. |
| DTE302 | XGBoost & LightGBM Model Optimization Lab | Practical | Tune hyperparameters, optimize learning rates and cross-validate gradient boosted ensembles on tabular datasets. |
| DTE303 | Distributed Computing Principles & Apache Spark Architecture | Theory | Analyze Spark execution plans, Resilient Distributed Datasets (RDDs), data shuffling and worker node partitioning. |
| DTE304 | PySpark Distributed Data Processing & Spark SQL Lab | Practical | Process multi-gigabyte datasets using PySpark DataFrames, lazy evaluations and distributed SQL queries on clusters. |
| DTE305 | Time Series Analysis, ARIMA Modeling & Forecasting | Theory | Model stationarity, autocorrelation (ACF/PACF), seasonal decomposition, ARIMA and exponential smoothing. |
| DTE306 | Time Series Forecasting Lab (Prophet & Statsmodels) | Practical | Build automated time-series forecasting models with Statsmodels and Meta Prophet for trend and seasonality. |
| DTE307 | Cloud Data Warehousing Architecture (Snowflake & BigQuery) | Theory | Analyze decoupled storage and compute, columnar storage clustering, micro-partitions and virtual warehouses. |
| DTE308 | Cloud Data Warehouse Modeling & dbt Transformation Lab | Practical | Construct dimensional star-schema models and execute automated dbt data transformations in Snowflake/BigQuery. |
| DTE309 | Quantitative Research Design & Econometric Methods in Data Engineering | Theory | Formulate regression discontinuity, instrumental variables and panel data models for causal inference. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE401 | Deep Learning for Unstructured Data & Neural Network Foundations | Theory | Analyze feedforward perceptrons, backpropagation gradients, activation functions and loss surface dynamics. |
| DTE402 | PyTorch Deep Learning & Tensor Operations Lab | Practical | Build and train multi-layer neural networks for classification and regression tasks using PyTorch and CUDA. |
| DTE403 | Natural Language Processing & Text Analytics Foundations | Theory | Deconstruct TF-IDF term vectors, word embeddings, tokenization, topic modeling (LDA) and sentiment classification. |
| DTE404 | NLP Text Mining & Sentiment Classification Lab | Practical | Extract named entities, build topic models and fine-tune transformer sentiment classifiers using Hugging Face. |
| DTE405 | Real-Time Streaming Architectures & Event Hubs (Kafka) | Theory | Architect real-time streaming pipelines, Kafka topic partitions, consumer groups and stream-table duality. |
| DTE406 | Kafka Event Streaming & PySpark Streaming Lab | Practical | Consume real-time simulated telemetry streams with Kafka and execute windowed aggregations in PySpark Streaming. |
| DTE407 | Data Governance, Lineage & Metadata Management | Theory | Implement data cataloging, automated lineage tracking, data stewardship frameworks and regulatory auditability. |
| DTE408 | Automated Data Pipelines with Apache Airflow Lab | Practical | Author Directed Acyclic Graphs (DAGs) in Apache Airflow with task dependencies, automated retries and alerts. |
| DTE409 | Industrial Data Engineering Team Project: Analytics Solution Studio | Practical | Deliver a complete production analytics solution combining data ingestion, transformation, modeling and reporting. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE501 | Causal Inference, Counterfactuals & Directed Acyclic Graphs | Theory | Evaluate potential outcomes frameworks, Judea Pearl's do-calculus, confounders and propensity score matching. |
| DTE502 | Causal Modeling & Propensity Score Matching Lab | Practical | Implement propensity score matching, difference-in-differences and instrumental variable models in Python (DoWhy). |
| DTE503 | Unsupervised Learning, Clustering & Dimensionality Reduction | Theory | Derive k-means, hierarchical agglomerative clustering, DBSCAN, t-SNE and UMAP manifold projections. |
| DTE504 | High-Dimensional Clustering & Customer Segmentation Lab | Practical | Execute PCA dimensionality reduction and density-based clustering to discover latent behavioral segments. |
| DTE505 | Strategic Enterprise Analytics, ROI & Decision Science | Theory | Calculate expected monetary value of models, false positive costs, churn prevention ROI and executive metrics. |
| DTE506 | Automated Machine Learning (AutoML) & Model Governance Lab | Practical | Deploy AutoML frameworks (FLAML, AutoGluon) and establish model registry governance with MLflow tracking. |
| DTE507 | Swiss & Global Data Protection Compliance (FADP & GDPR) | Theory | Audit analytical datasets for PII exposure, differential privacy noise injection and compliant data retention. |
| DTE508 | Data Quality Monitoring with Great Expectations Lab | Practical | Define automated data quality assertions, schema validations and alerting pipelines with Great Expectations. |
| DTE509 | Senior Data Engineering Capstone Project: Problem Formulation & Scoping | Practical | Author comprehensive problem statements, data acquisition protocols, metric definitions and validation plans. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DTE601 | Emerging Frontiers in Data Science & Foundation Models | Theory | Evaluate generative data augmentation, synthetic tabular data generation, LLM agents for data science and frontiers. |
| DTE602 | Senior Data Engineering Capstone Project: End-to-End Pipeline Build | Practical | Engineer the complete data pipeline, model training workflows and automated inference endpoints for the capstone. |
| DTE603 | Senior Data Engineering Capstone Project: Empirical Evaluation & Benchmarking | Practical | Conduct thorough statistical cross-validation, sensitivity analysis, bias testing and benchmark against baselines. |
| DTE604 | Executive Data Leadership & Boardroom Storytelling | Theory | Communicate model assumptions, uncertainty distributions and strategic data initiatives to C-suite executives. |
| DTE605 | Production Deployment, Streamlit / FastAPI Serving Lab | Practical | Deploy the trained analytical model as an interactive web app and REST API with containerized Docker packaging. |
| DTE606 | Societal Implications of Big Data, Surveillance & Human Dignity | Theory | Analyze data surveillance capitalism, autonomous predictive profiling, predictive justice ethics and dignity. |
| DTE607 | Industry Technical Data Portfolio & Model Defense | Practical | Defend statistical methodology, feature selection choices and business impact before an expert industry review board. |
| DTE608 | Peer Review, Reproducibility & Scientific Dissemination | Theory | Author reproducible Jupyter research notebooks, methodology whitepapers and open-source data artifacts. |
| DTE609 | Final Data Engineering Capstone Project Demonstration & Oral Defense | Practical | Demonstrate the live production data science system and successfully pass an oral academic dissertation examination in Data Engineering. |
Secondary School Diploma / Swiss Matura / International Baccalaureate or recognized foreign upper-secondary certificate. English proficiency IELTS 6.0+ 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.