Python for Data Analysis
Go from zero Python to answering real business questions with pandas.
- 4.6 (1,730 ratings)
- 15,200 learners
- Beginner
- 7h 10m total
- Updated May 2026
What you’ll learn
- Write the core Python an analyst needs: types, lists, dicts, functions
- Load, inspect and select data with pandas DataFrames
- Clean missing values, text, money strings, dates and duplicates
- Summarise and join tables with groupby, merge and pivot_table
- Analyse trends over time with resample and the dt accessor
- Answer a business question end to end and make it reproducible
Who it’s for
Spreadsheets break down when the data gets messy, the questions repeat every month, or someone asks how you got that number. This course teaches you Python and pandas from the first line of code, using the real tables of Cartwheel, an online store: orders, products, customers, returns and a deliberately messy export like the ones that land in your inbox.
You learn the habits working analysts rely on: inspect data before you trust it, clean it with code you can rerun, check every join, and explain a number in plain words. The course ends with a full case study, why Cartwheel's Q4 2025 revenue jumped, and a short guide to making your analysis reproducible.
- Who it’s for
- Career switchers and spreadsheet users who want to analyse data with Python, starting from zero programming experience.
- Before you start
- None. Comfort with a spreadsheet (sums, filters, pivot tables) helps but is not required.
Hands-on practice
- Run real Python and pandas in your browser — no install.
- Guided exercises: spot the bug, fill the command, order the steps.
Course content
5 sections · 24 lessons · 7h 10m total
- Why Analysts Use Python10 min
- Values, Types and Variables16 min
- Lists and Dictionaries18 min
- Loops, Conditions and Comprehensions18 min
- Functions, f-strings and Reading Errors20 min
- Section checkpoint5 questions
- Series and DataFrames16 min
- Loading and Inspecting a CSV18 min
- Selecting with loc and iloc18 min
- Filtering and Sorting Rows18 min
- Adding and Changing Columns18 min
- Section checkpoint5 questions
- Missing Values18 min
- Cleaning Text Columns16 min
- Turning Money Strings into Numbers16 min
- Parsing Messy Dates20 min
- Duplicates and a Repeatable Cleaning Function18 min
- Section checkpoint5 questions
- groupby and Named Aggregation20 min
- Joining Tables with merge20 min
- Reshaping with pivot_table and melt18 min
- Time Series: resample and the dt Accessor20 min
- Ranking and Binning16 min
- Section checkpoint5 questions
- Descriptive Statistics and Distributions18 min
- Charts with pandas and matplotlib16 min
- Case Study: Why Did Q4 2025 Revenue Jump?26 min
- Reproducible Analysis18 min
- Section checkpoint5 questions
Your instructor
Liam Patel
Lead Data Scientist · Datawheel
- 4.7 instructor rating
- 32,900 learners
- 3 courses
Liam started as a business analyst who got tired of copying numbers between spreadsheets, so he learned SQL, then Python, then enough statistics to know when a chart is lying. At Datawheel he leads the team that builds forecasting and churn models for retail clients.
What learners say
4.6
1,730 ratings
Ahmed Farouk
8 months ago
I came from Excel and had never written a loop. By the end I'd built a cleaning function for the messy orders export and explained the Q4 revenue jump to my manager with one groupby.
Chloe Martin
6 months ago
The date-parsing lesson alone saved me a week at work. Seeing 05/03 and 03/05 in the same column, and learning how to tell them apart, is exactly the mess I deal with.
Kwame Mensah
3 months ago
Clear, patient, and the in-browser exercises run fast. The first section drags if you already know basic Python, so I'd like a way to skip ahead.