Start with Python.
Think like a data analyst.

Before lesson 1

1. Install Python 3 from the official Python website. On Windows, enable the installer option to add Python to PATH if offered.
2. Open IDLE, create a new file, type print("Hello"), save it as lesson_001.py in a practice folder, and choose Run → Run Module.
3. If you prefer a terminal, verify python --version, then run python lesson_001.py. On Windows, py may also launch Python.
4. For lesson 25 onward, install packages in your practice environment using the command below. SQLite, csv and json are included with Python.

python -m venv .venv
.venv\Scripts\python.exe -m pip install numpy pandas matplotlib scikit-learn
.venv\Scripts\python.exe lesson_025.py

On macOS or Linux, use python3 -m venv .venv, then .venv/bin/python -m pip install numpy pandas matplotlib scikit-learn and .venv/bin/python lesson_025.py. On phones and tablets, read lessons here and run downloaded code in a separate Python environment appropriate to your device.

These are Windows terminal commands, not Python statements. Keep your practice scripts outside the protected dashboard file. All examples are embedded and can be downloaded as .py files; Python runs in your editor or terminal, not inside this HTML.

Your daily routine

1. 10 min: recall the last lesson without looking.
2. 20 min: read the lecture; explain each new term.
3. 20 min: predict and run the worked example.
4. 25 min: complete the scenario task independently.
5. 10 min: test edge cases, score yourself, and save notes.

Every sixth lesson in a module is a checkpoint. A score below 80 means revisit the weak skill; keep studying on the scheduled dates and use the next session's recall block for review. Sundays remain free.

Phases: 1–24 Python fundamentals; 25–48 NumPy and pandas; 49–72 SQL, statistics and visualization; 73–104 pipelines, introductory ML, capstone and interviews. These are learning phases within four calendar months, not equal-length calendar months.

Target role: junior data analyst. Data engineering and data science topics are introductions, with deeper study needed for specialist roles.

Daily lesson delivery

Automation setup is being verified.

The HTML opens any lesson immediately. Codex delivers the scheduled lesson in the original task. This page cannot run a background scheduler while closed.

Optional direct controls: in Chrome or Edge, choose the outputs folder containing this HTML with “Connect dashboard folder.” Controls then save learning-control.json beside this file for the daily automation to read. Reconnect after reopening if needed. Browser progress alone is not visible to Codex.

No shared folder connected. Scheduled delivery uses its existing saved state.

Pausing through this page tells scheduled runs to stay quiet; it does not disable the underlying schedule. To change the 3:00 pm time or disable the schedule itself, use Codex scheduled-task controls or ask in the original task.

Backups and storage

Completion, scores and notes are stored in this browser. Clearing browser data, changing browser or moving the HTML may make that storage unavailable. Export progress regularly. Imports merge records and never erase unrelated lessons.

Calendar export contains all 104 sessions with a 3:00 pm start and a reminder. Import it once to avoid duplicates. A calendar event does not generate a lesson by itself.

No analytics, remote scripts, tracking pixels or external accounts are used by this dashboard. Links to reference websites open only when you choose them. PrivacyHawk manages personal exposure rather than local HTML progress.