Ep02 - Python for Data скачать в хорошем качестве

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. #DataOverCoffee #DataPodcast #Python #DataAnalytics #Auckland #NewZealand #Excel #PowerBI #SQL #Automation #MachineLearning #SoftcraftStudio #ExcelinBI #LearningData #DataEngineering #BusinessAnalytics #BeginnersInData #CareerInData