Ep02 - Data Over Coffee - Python for Data

Data Over Coffee — Ep. 2: Python for Data People (why it wins, where it struggles, and how to start)

Welcome back to Data Over Coffee. We’re Arjun Kataria and Ramon Zamorano, two Auckland-based data folks who turn complex ideas into practical chats you can enjoy over a cup of tea or coffee.

In this episode we dive into Python: why it keeps winning in analytics, automation and AI, what it actually does better than spreadsheets and SQL alone, where its limits show up, and simple ways for non-programmers to get productive fast. No heavy code walk-throughs, just clear stories, useful ideas and honest takes you can apply at work.

What you’ll hear
- When to reach for Python vs Excel, Power Query and SQL
- Libraries and data frames in plain language
- Starter projects that save hours: Excel joins, Jira exports, API pulls
- Python vs R for analysis
- Tooling to learn and ship: VS Code, Anaconda, Replit, LinkedIn Learning
- Where Python struggles and when to use something else

Suggest a topic: We want your questions and ideas. Comment with topics you’d like us to cover next, or share a challenge you’re facing with data at work.

Connect with us
- Ramon’s company: Excel in BI — https://www.excelinbi.com — https://linkedin.com/in/razamorano

- Arjun’s company: Softcraft Studio — https://softcraftstudio.net — https://linkedin.com/in/arjunkataria

Both of us are based in Auckland, New Zealand.

Chapters
00:00 Intro
00:11 Welcome back to Data Over Coffee
00:26 Why Python shows up in every data role
01:01 Our goal for the podcast and today’s topic
02:07 Arjun’s first steps in Python
03:32 Ramon’s journey from assembler to Python
07:28 SQL vs Python in data work
08:36 Why libraries make Python feel simple
09:50 Performance gains with modern Python
12:15 Thinking in logic: if/else and small wins
14:12 Open-source ecosystem and tooling
15:07 Data frames mindset (and R comparison)
18:02 APIs, scraping and Excel automation examples
20:56 Apps vs pipelines and maintainability
22:22 Python’s sweet spots: transforms and ML
24:49 Where to start if you’re a BA, QA or Test Analyst
26:48 Excel pain points Python fixes fast
30:55 Jira export → automated report workflow
31:44 When to use Power Query instead
33:12 Learning options: Replit and LinkedIn Learning
35:40 Takeaways and scalability
37:35 Portability across AWS, Azure and GCP
38:44 Next episode preview: AI in data
39:21 Casual wrap and sign-off

Disclaimer: No sponsors. Views are our own, based on hands-on experience.

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Видео Ep02 - Data Over Coffee - Python for Data канала Data Over Coffee Podcast