Welcome to Autonomous Econ
The mission of Autonomous Econ is to empower the common analyst who doesn’t have a computer or data science background.
This means concise, easy-to-follow tutorials that deliver big results while staying light on technical jargon.
If you're handling economic, business, or financial data daily, this newsletter will reveal how data science tools can simplify your job and elevate the quality of your analysis.
Anyone working in economics, government, or business should be able to benefit from programming languages like Python, which can automate tedious tasks.
Python opens up opportunities to quickly create compelling data visualizations and build accurate prediction models that add value.
All of this is achievable, even if you're starting with little or no knowledge of Python programming. Recent advancements in AI-supported coding and low-code/no-code tools have significantly lowered the entry barrier.
Become a savvier analyst so you can take your projects to the next level and boost your profile within your organization.
About me
I'm Martin Wong, and I spent a sizeable chunk of my career as a macroeconomist, initially as a forecaster at the New Zealand Treasury and later at the Reserve Bank (central bank) of New Zealand.
More recently, I transitioned to data science in the tech sector, helping large retailers improve their forecasting capabilities to optimize pricing and reduce unnecessary waste.
My journey has provided me with unique insights into how data science tools can address the common challenges I faced as an economist. I started this newsletter, partly as a way to document my learning and projects, and I’m sure you will find something useful amongst it too.
Where you can find me:
What you will get from this newsletter
You will receive weekly articles covering tips on how to use data science packages and Python scripts for your workflows. These articles will be written to be intuitive and accessible for anyone who is just starting their journey in Python.
Posts will roughly be split between practical guides (80 percent) and data journalism pieces where I demonstrate the tools (20 percent).
Check out some of my recent posts here:
Other articles in the pipeline include:
Complex data visualization (think scrolling data stories, geo plots, animations).
How to build a range of proven machine learning models for economic data.
How to automate prediction models and dashboards on a schedule or after a trigger, such as a new data point.
Automating model selection and fine-tuning.
Web scraping unique and novel datasets.
Join my community
Participate in the comments section - I welcome any feedback. Feel free to post questions on specific pain points that you’re facing in your work.
