https://github.com/denisecase/cintel-06-continuous-intelligence
Professional Python project using Polars for continuous intelligence.
https://github.com/denisecase/cintel-06-continuous-intelligence
continuous-intelligence data-analytics decision-intelligence github-actions operational-analytics professional-python python reproducible-workflows src-layout
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Professional Python project using Polars for continuous intelligence.
- Host: GitHub
- URL: https://github.com/denisecase/cintel-06-continuous-intelligence
- Owner: denisecase
- License: mit
- Created: 2026-03-07T16:24:13.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-04-06T15:34:29.000Z (about 2 months ago)
- Last Synced: 2026-04-06T17:22:38.683Z (about 2 months ago)
- Topics: continuous-intelligence, data-analytics, decision-intelligence, github-actions, operational-analytics, professional-python, python, reproducible-workflows, src-layout
- Language: Python
- Homepage: https://denisecase.github.io/cintel-06-continuous-intelligence/
- Size: 83 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
- Agents: .github/AGENTS.md
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README
# cintel-06-continuous-intelligence
[](https://denisecase.github.io/cintel-06-continuous-intelligence/)
[](https://github.com/denisecase/cintel-06-continuous-intelligence/actions/workflows/ci-python-zensical.yml)
[](#)
[](./LICENSE)
> Professional Python project for continuous intelligence.
Continuous intelligence systems monitor data streams, detect change, and respond in real time.
This course builds those capabilities through working projects.
In the age of generative AI, durable skills are grounded in real work:
setting up a professional environment,
reading and running code,
understanding the logic,
and pushing work to a shared repository.
Each project follows the structure of professional Python projects.
We learn by doing.
## This Project
This project brings together several techniques used in continuous intelligence systems.
The goal is to copy this repository,
set up your environment,
run the example analysis,
and explore how monitoring techniques can be combined
to assess the current state of a system.
Run the example pipeline, read the code, and see how:
- raw system metrics are transformed into useful signals
- anomalies are detected in those signals
- monitoring results are summarized to assess system health
This project demonstrates how monitoring data can support operational awareness and decision-making.
This module serves as a capstone:
it encourages you to combine techniques developed
in earlier modules into a simple continuous intelligence pipeline:
- Module 2. anomaly detection
- Module 3. signal design
- Module 4. rolling monitoring
- Module 5. drift comparison
- Module 6. system assessment (integration).
## Data
The example pipeline reads system metrics from:
`data/system_metrics_case.csv`
Each row represents one observation of system activity.
The pipeline derives signals such as
**error rate** and **average latency**,
checks for anomalous conditions,
and produces a summary assessment of system behavior.
The dataset includes a short period of degraded performance
so that monitoring signals and anomaly detection
produce visible results.
## Working Files
You'll work with just these areas:
- **data/** - it starts with the data
- **docs/** - tell the story
- **src/cintel/** - where the magic happens
- **pyproject.toml** - update authorship & links
- **zensical.toml** - update authorship & links
## Instructions
Follow the [step-by-step workflow guide](https://denisecase.github.io/pro-analytics-02/workflow-b-apply-example-project/) to complete:
1. Phase 1. **Start & Run**
2. Phase 2. **Change Authorship**
3. Phase 3. **Read & Understand**
4. Phase 4. **Modify**
5. Phase 5. **Apply**
## Challenges
Challenges are expected.
Sometimes instructions may not quite match your operating system.
When issues occur, share screenshots, error messages, and details about what you tried.
Working through issues is part of implementing professional projects.
## Success
After completing Phase 1. **Start & Run**, you'll have your own GitHub project, running on your machine, and running the example will print out:
```shell
========================
Pipeline executed successfully!
========================
```
And a new file named `project.log` will appear in the project folder.
## Command Reference
The commands below are used in the workflow guide above.
They are provided here for convenience.
Follow the guide for the **full instructions**.
Show command reference
### In a machine terminal (open in your `Repos` folder)
After you get a copy of this repo in your own GitHub account,
open a machine terminal in your `Repos` folder:
```shell
# Replace username with YOUR GitHub username.
git clone https://github.com/username/cintel-06-continuous-intelligence
cd cintel-06-continuous-intelligence
code .
```
### In a VS Code terminal
```shell
uv self update
uv python pin 3.14
uv sync --extra dev --extra docs --upgrade
uvx pre-commit install
git add -A
uvx pre-commit run --all-files
# repeat if changes were made
git add -A
uvx pre-commit run --all-files
uv run python -m cintel.continuous_intelligence_case
uv run ruff format .
uv run ruff check . --fix
uv run zensical build
git add -A
git commit -m "update"
git push -u origin main
```
## Notes
- Use the **UP ARROW** and **DOWN ARROW** in the terminal to scroll through past commands.
- Use `CTRL+f` to find (and replace) text within a file.