BANA 7025: Data Wrangling with Python
Welcome
Welcome to BANA 7025: Data Wrangling with Python — a 7-week graduate course that teaches you how to work with data in Python from first principles through applied wrangling, visualization, and reproducible workflows.
What this course is about
Data rarely arrives in the form you need. Before any analysis, modeling, or reporting can happen, someone has to clean it, reshape it, join it, and make sense of it. That work is data wrangling — and it is the most common, most time-consuming, and most underestimated skill in applied data science.
This course teaches you to do that work fluently in Python.
By the end of BANA 7025, you will:
- Set up and navigate a Python environment for data science
- Read data from CSV, Excel, databases, and the web
- Inspect, filter, sort, and select data with pandas
- Clean messy, missing, and inconsistently formatted data
- Aggregate and summarize data across groups
- Join and reshape multiple tables
- Visualize patterns with matplotlib, seaborn, and Bokeh
- Conduct exploratory data analysis as a systematic workflow
- Write reusable functions and reproducible workflows
The semester project
Each module connects to a semester-long project built around the Complete Journey retail transaction dataset — a rich, multi-table dataset from a real grocery retailer that tracks household purchases over time. You will load, clean, join, aggregate, and visualize this data week by week, arriving at a final analytic dataset and summary report by the end of the course.
How to use this book
This book is the primary reading material for BANA 7025. Each chapter maps to one or more lessons in the course:
| Module | Topics | Chapters |
|---|---|---|
| 1 | Getting started | 1 – 3 |
| 2 | Python data science ecosystem | 4 – 6 |
| 3 | DataFrames & importing data | 7 – 9 |
| 4 | Data manipulation | 10 – 12 |
| 5 | Data visualization & EDA | 13 – 15 |
| 6 | Creating efficient code | 16 – 18 |
| 7 | Semester project | — |
Each chapter includes:
- Conceptual explanations of the topic
- Worked Python examples you can run in Jupyter or Google Colab
- Your Turn exercises to check your understanding
- Semester project checkpoints that apply the concepts to the Complete Journey data
Resources
Each module has a landing page that lists all the content for that week. For example, Module 1 includes:
- Lecture slides — Reveal.js deck for the week
- Chapter notebooks — Colab-ready notebooks paired with each chapter’s code
- Lab notebook — Thursday lab assignment
- Cheat sheet — quick reference card for the module’s key concepts and syntax
Every module follows this same structure. Start at the module landing page and work through the resources in order.
Additional course resources:
- Semester project — full project overview and weekly checkpoints
- AI Tutor — grounded in course content, available anytime
Getting help
The AI tutor embedded in this platform is trained on this textbook, the slides, and the example notebooks. It can help you understand concepts, debug code, and reason through problems — but it will not complete graded assignments for you.
For questions about grading, logistics, or policies, contact the course instructor directly.