The essential Python tools to quickly impress your boss
My curated resource for non-data scientists working in economics, business, and finance.
Python has a rich library of packages (100K+), which is not surprising because it’s a general-purpose language used by many different professions: software engineers, developers, data scientists, and data engineers.
For analysts who are new to Python and work primarily with economic, financial, or business data, it can take a long time to filter out the most relevant and best tools to learn first.
Many Python tutorials also cater to traditional data science career paths and cover topics like big data engineering, neural networks, and machine learning pipelines.
However, analysts with a non-tech background are likely more interested in how Python can automate day-to-day workflows or provide quick wins that can’t be achieved with regular tools like Excel.
This might involve automating regular reports, extracting data from PDFs or web pages, creating unique and professional charts or dashboards, or developing a small time-series prediction model.
With the help of AI-coding tools, they want to quickly develop prototypes using the appropriate libraries or packages to impress a manager or client.
That’s why today I’m sharing my website of curated resources and links to guide you in using Python for common tasks.
This website contains the most useful guides I’ve come across in the last several years and during my research for my posts.
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The Python for Econ Starter Pack
One year ago, I wrote a post providing a starter pack on how to learn Python. Most of the advice in it still stands, but I've come across many other useful resources since then, and a Notion website seemed like the best way to collect them.
If you’re relatively new to Python, go to the ‘Start here’ page, which covers what I consider the essential skills for starting any Python coding project and performing basic data analysis and visualization.
The Python Fundamentals section goes deeper into other useful tools, like automating scripts, accessing data via APIs, and how to utilize the core Python data libraries (pandas, numpy, scipy).
The Data Viz / Dashboards tab includes resources to help you create polished charts, automated reports, and interactive dashboards.
Prediction Algorithms section contains intuitive guides on econometrics and machine learning. The focus is on simple, proven time series models, so it’s light on neural networks, which can often be overkill.
Lastly, the Gen AI section focuses on tools for integrating large language models into your workflows via APIs from the likes of OpenAI and DeepSeek.
The site will be updated from time to time, and I hope to add a web scraping tab soon.
What’s coming up in 2025…
I’ve been on a brief hiatus due to parental leave, which gave me a chance to reflect on the focus of this newsletter in the coming year.
First off, it’s been about a year since I started this newsletter, and I’m really grateful to everyone who subscribed and for the kind words of support—thank you!
In the near term, I plan to publish content focused on powerful productivity hacks for common problems. This includes topics like automated web scraping, text analysis, generating professional reports/dashboards, and building accurate prediction models quickly.
AI agents are something I’ve been exploring in recent months, and the tools and frameworks now seem mature enough to easily build powerful prototypes. Expect posts on how to create custom agents to handle tasks like economic research, data exploration, and forecasting.
I’ll also share how to use alternative data types to complement traditional economic and financial data, such as sentiment analysis data and satellite data.
The focus will be on making the content more accessible using digestible and minimal code examples. This approach should allow me to share valuable content more regularly.
