Why Analysts Use Python

Every month, someone at a company exports a spreadsheet, filters it, fixes the same broken dates, builds the same pivot table and pastes the numbers into a slide. It takes an afternoon, and if the export changes slightly, the numbers quietly go wrong. Python turns that afternoon into a script: you write the steps once, and rerunning them on next month’s file takes seconds.

This course teaches you exactly the Python a data analyst uses, from zero. No computer science detours: every concept appears because you need it to answer a business question.

The company you will work for

Cartwheel is an online store selling home and outdoor goods in eight countries: Egypt, the UAE, Saudi Arabia, Jordan, the UK, Germany, the US and Canada. Its data covers January 2024 to December 2025, and it looks like real company data: an orders table, order_items with prices and discounts, products, customers, returns, support tickets and website sessions. There is also orders_messy.csv, an export with mixed date formats, dollar signs in the totals and duplicate rows, which you will clean in Section 3.

Your running assignment comes from Cartwheel’s head of growth: revenue in Q4 2025 was far higher than any quarter before. Was it Black Friday, more customers, bigger baskets, or a data mistake? By the final section you will answer that with evidence, and every lesson before it gives you one tool you need for the job.

How the code in this course runs

Code blocks with a Run button execute real Python 3.13, with pandas 2.x, in your browser. The Cartwheel CSV files sit in the working directory, so you can open them by name. Output appears only when you print() it. Here is a first taste; you do not need to understand every line yet:

import pandas as pd

orders = pd.read_csv("orders.csv")
print(len(orders), "orders")
print(orders["status"].value_counts())

Four lines, and you know Cartwheel has 2,066 orders and how many were delivered, cancelled or returned. By the end of Section 2 you will write lines like these yourself and know exactly what each one does.

Most lessons end with an exercise. You write code in the editor, press Check, and a set of automatic checks tells you which parts are right. Hints are there when you are stuck, and the full solution with an explanation appears once you solve it or ask for it.

The loop every analysis follows

The course is built around one loop. Each section strengthens one part of it.

Section 1, the one you are starting, gives you the plain Python that every later stage is written in: values, lists, dictionaries, loops, functions, and how to read an error message without panic.

Next you meet the building blocks of every Python program: values, their types, and the variables that name them.