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Project Instructions

WEDNESDAY: Complete Workflow Phase 1-3

Follow the instructions in ⭐ Workflow: Apply Example.

Complete:

  1. Phase 1. Start & Run – copy the project and confirm it runs
  2. Phase 2. Change Authorship – update the project to your name and GitHub account
  3. Phase 3. Read & Understand – review the project structure and code

FRIDAY/SUNDAY: Complete Workflow Phases 4-5

Complete:

  1. Phase 4. Make a Technical Modification
  2. Phase 5. Apply the Skills to a New Problem

Topic

Signal design for monitoring system behavior.

In this project, you will transform raw system metrics into derived signals that are easier to interpret and monitor.

Signals help analysts detect problems, understand performance, and monitor how systems behave over time.

Learning Objectives

After completing this project, you should be able to:

  • Explain why analysts design signals from raw data
  • Create derived metrics using simple calculations
  • Add new signal columns to a DataFrame
  • Run and validate a professional Python project
  • Interpret signals that describe system behavior

Example Code

The example file is located in:

src/cintel/signal_design_case.py

It demonstrates:

  • reading system metrics from a CSV file
  • creating derived signals from raw measurements
  • protecting calculations from division-by-zero
  • writing signal outputs to an artifacts file
  • logging the pipeline process

Run the example and review the code before creating your own version.

Dataset

The example dataset is located in the data/ folder.

Example fields include:

  • requests
  • errors
  • total_latency_ms

Each row represents a system observation.

Signals are created from these measurements to help monitor system behavior.

Your Phase 4: Technical Modification Task

Using the example as a guide:

  1. Copy src/cintel/signal_design_case.py.
  2. Rename the copy to src/cintel/signal_design_yourname.py.
  3. Run your copied file to confirm it executes correctly.
  4. Modify the file by adding at least one additional signal.

Possible signals include:

  • error rate per request
  • average latency per request
  • requests per time unit
  • ratio-based metrics

Your new signal should be added as a new column in the DataFrame.

Then:

  • run the project
  • confirm the new signal appears in the output artifact
  • confirm the program logs useful messages

The goal of this phase is to verify that you can modify a working project and observe the result.

Phase 5: Apply the Skills

In Phase 5 you will apply signal design to a new situation.

Possible approaches include:

  • modifying the dataset
  • introducing additional system signals
  • applying the method to a different monitoring scenario

Examples might include:

  • website traffic monitoring
  • application performance monitoring
  • service request analysis
  • sensor or environmental measurements

Document your changes in the project documentation (docs/):

  • describe the signals you added
  • explain why they are useful
  • describe what they reveal about the system

Signal design is a key step in continuous intelligence, because monitoring systems depend on signals that clearly describe system behavior.

If you would like to apply these skills to a real dataset instead of the provided example data, see suggested datasets:

https://denisecase.github.io/pro-analytics-02/reference/datasets/cintel/