DSC3103 · 9 modules

Data Science: Systems & Technologies

Trusted data systems: cleaning, pipelines, warehouses, orchestration, Spark, streaming and serving.

Start: Data Profiling, Validation & Cleaning Mock exam Revision guide
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  1. 1

    Data Profiling, Validation & Cleaning

    Turn messy field CSVs into a trusted analytical dataset (Lab 2)

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  2. 2

    Pipelines: ETL vs ELT, Parquet & Idempotency

    Build modular, repeatable pipelines with columnar storage (Lab 3)

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  3. 3

    Analytical Star Schemas & DuckDB

    Model facts, dimensions & OLAP queries for fast analytics (Lab 4)

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  4. 4

    Workflow Orchestration with Apache Airflow

    Schedule DAGs, manage dependencies & handle retries (Lab 5)

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  5. 5

    Distributed Batch Processing with PySpark

    Partitions, lazy evaluation, shuffles & caching (Lab 6 / Lab 8)

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  6. 6

    Streaming, Event-Time Windows & Watermarks

    Process unbounded events with out-of-order arrivals (Lab 9 Part 1)

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  7. 7

    From Model to Data Product: FastAPI & Docker

    Serve ML models with zero training-serving skew (Lab 9 Part 2 / Lab 10)

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  8. 8

    Practice Exam (sample_qtn): Milk Validation & Collisions

    Master rule ordering, record_id collisions & row conservation (sample_qtn.pdf)

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  9. 9

    Practical Test 02 (dsc3103_test_02): Milk Star Schema & Analytics

    Conquer unknown dimension keys, 2-stage capacity SQL & partition pruning

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