Section 1 of 7 · Programming & Data Foundations

Python & Data Tools

Get productive with real datasets before diving into interpreter internals or extended theory. This short on-ramp introduces notebooks, practical Python, NumPy, pandas and diagnostic plots so the learner can inspect data and create evidence from the first week.

4 units 5 topics 0/4

Units

4 in this section

1.1 Notebook workflow, Python essentials and reproducible runs
implement · explain
5 h
1.2 NumPy arrays: shape, dtype and vectorized operations
explain · implement · measure
4 h
1.3 Inspect and transform tabular data with pandas
implement · measure
5 h
1.4 Visualize a dataset and write a data audit
measure · explain
3 h

Before moving on

Checkpoint

From a clean notebook, load the UCI Bike Sharing hourly data, inspect its schema and quality, produce three diagnostic plots, and write a brief data audit. The notebook must run from top to bottom without hidden interactive state.

Deliberately out of scope

Leave for later

CPython object layout, reference counting and import machinery
Advanced NumPy stride tricks and custom array kernels
Complex statistical visualizations and dashboard frameworks

Resources for this section

Shared by every unit above

Kaggle Learn — Python
Kaggle Learncourse

primary

Complete the course lessons on variables, functions, conditionals, lists, loops, strings, dictionaries and external libraries. Treat it as the practical Python vocabulary needed for the next units.
The Python Tutorial
Python Software Foundationreference

reference

Use as a reference for control flow, data structures, modules, files and exceptions. It assumes some prior programming experience; use Kaggle Learn for the first pass if the syntax is new.
Google Colab FAQ — free resources and limits
Google Researchnotebook-environment

support

Use a hosted notebook for the first exercises if local setup is a distraction. The free tier has variable, non-guaranteed compute and GPU access; keep every exercise small enough to run on CPU.
NumPy Quickstart Tutorial
NumPydocumentation

primary

Study array creation, shape, indexing, universal functions, broadcasting and basic operations. Write down shapes and dtypes before changing an array.
Kaggle Learn — Pandas
Kaggle Learncourse

primary

Complete the short exercises on reading data, selecting, summary functions, grouping, sorting, data types, missing values and combining tables.
pandas Getting Started Tutorials
pandas Development Teamdocumentation

required

Use the official tutorials as a reference for reading and writing tables, selecting rows and columns, creating columns, summary statistics, joins and time-series handling.
Matplotlib Quick Start Guide
Matplotlibdocumentation

support

Use the Figure/Axes workflow to make labeled line plots, histograms and scatter plots. Keep plots diagnostic rather than decorative.
Bike Sharing Dataset
UCI Machine Learning Repositorydataset

required

Use the hourly hour.csv data for the two linked starter projects. Cite Fanaee-T (2013), DOI 10.24432/C5W894, and the CC BY 4.0 licence. cnt is the target; casual and registered are components of that target and must never be used as input features.

Apply the section

Mini-project · A reproducible audit of the Bike Sharing dataset (3 to 4 h)

Explore the UCI hourly Bike Sharing dataset before fitting a model. Create a notebook that records the dataset source and license, checks its schema and quality, summarizes demand, and visualizes demand by hour, weekday and weather. This is an exploratory audit, not a prediction exercise. Keep target-related columns identified and do not train a model in this project.

Steps

Download the hourly hour.csv file and record the UCI citation and CC BY 4.0 license.
Load the table in a fresh notebook; inspect shape, dtypes, missing values, duplicate rows and the data dictionary.
Compute small summaries and create three labeled plots for hourly demand, weekdays and weather conditions.
Write an audit that separates observed patterns from hypotheses and flags casual, registered and cnt as related target fields.

Deliverables

A notebook that runs top-to-bottom from a clean kernel.
A compact data dictionary and quality summary.
Three labeled diagnostic plots with written interpretations.
A README note recording the source and license.

Checks

reproducibility · The notebook runs from a clean kernel without hidden state.
data-quality · Schema, null and duplicate checks are recorded.
attribution · UCI citation and CC BY 4.0 attribution are included.

Rubric

Criterion	Pass	Strong
Data understanding	Reports the dataset structure, target-related columns and basic data-quality findings.	Explains how each finding changes the permissible modeling choices.
Exploratory evidence	Contains correctly labeled plots that answer stated questions.	Connects plot patterns to follow-up questions without making causal claims.
Reproducibility	Notebook executes cleanly from top to bottom.	Also records environment, source attribution and deterministic steps.

Feeds into: Build Your First Machine Learning Model: Bike Sharing Demand.