Why These Tools1 lesson and its Study toolkit.
Read the start-here guide, watch the lesson, then answer its quiz.
The lessonsin the order to take them.
Start herewhat the track is for, and its order.
One page to read alongside the first lesson.
You have learned Python's basic language: values, lists, loops and functions. This track shows how four libraries use that language to work with data and build a machine learning model. Module 0 is a short orientation; each library gets its own introduction later.
In Video 0.1, a chain's trading file passes through four jobs:
| Job you see | Library | What to remember for now |
|---|---|---|
| Read and organize rows from a file | pandas | It works with tabular data. |
| Draw the data so a pattern is visible | Matplotlib | It makes charts. |
| Work with the numbers underneath | NumPy | It provides arrays and numerical operations. |
| Train a model and make predictions | scikit-learn | It provides machine learning tools. |
That is the order of jobs in the example. The learning order starts with the numerical foundation, then returns to the other jobs:
- Module 1 · NumPy — arrays and calculations
- Module 2 · pandas — tables and data preparation
- Module 3 · Visualisation — Matplotlib and seaborn charts
- Module 4 · scikit-learn — the model interface
The same shop data returns throughout the track, so you can recognize what each tool adds to the work. You do not need to explain the code or the model score from Video 0.1 yet; the later modules build those ideas step by step.
Before moving on: Can you name one job for each library and explain why a Python loop alone is not the whole data workflow? If so, take the five-question check, then begin Tools 1.1 · Why NumPy.