Sale!
Reproducible Data Pipelines Practical

Reproducible Data Pipelines Practical

Original price was: $14.99.Current price is: $9.99.

Build a configured, validated, logged, and repeatable Python data pipeline that turns synthetic raw data into clean data, summaries, a figure, and reproducibility evidence.

Categories: ,

Description

Turn a collection of data-processing steps into a workflow that can be rerun, checked, explained, and shared.

The Reproducible Data Pipelines Practical guides you through a complete small-scale Python pipeline. You will generate a deterministic synthetic dataset, control file paths and accepted categories through YAML configuration, validate inputs before transformation, create analysis-ready data, calculate monthly summaries, publish a figure, and record execution evidence through logs and a JSON run manifest.

You will also test the workflow rather than simply running it. A controlled invalid value demonstrates fail-fast validation, while checksum comparisons help you distinguish stable analytical outputs from execution metadata that should change between runs.

What You Will Do

  • Organize raw data, processed data, configuration, code, and outputs.
  • Generate a deterministic synthetic service-events dataset.
  • Validate required columns, unique identifiers, categories, and numeric ranges.
  • Transform raw events into a clean analysis-ready table.
  • Produce monthly regional summaries and a publication-ready figure.
  • Capture logs, timestamps, environment details, row counts, and checksums.
  • Test idempotent reruns and a controlled validation failure.
  • Extend the pipeline with an additional validation rule.

What You Will Receive

  • Access to the browser-based online practical.
  • Downloadable project files with Python and Bash workflow scripts.
  • YAML pipeline configuration.
  • Synthetic input and generated example outputs.
  • A learner README and dependency file.
  • Quality-control evidence including a pipeline log and run manifest.

Who This Is For

This practical is designed for early-career data analysts, data scientists, researchers, and technical learners who can run basic Python commands and want to make their workflows more reliable and transferable.

Tools Used

Python, pandas, NumPy, Matplotlib, PyYAML, Bash, YAML, CSV, JSON, and Quarto.

The supplied dataset is synthetic and intended only for teaching and workflow testing.

Related products