Скачать или смотреть Data Over Coffee - Ep02 - 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 —   / razamorano  

Arjun’s company: Softcraft Studio — https://softcraftstudio.net —   / 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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