HeyJaredThe DIY Data Scientist
Learn real-world, do-it-yourself (DIY) analytics skills that make you stand out at work with weekly hands-on tutorials designed for ANY professional, including code and data.
- Indexed issues, last 90 days
- 13
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- Sep 30, 2026
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- Aug 18, 2026
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Data Visualization with Python Crash Course (opens the original)
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You may find this surprising, unfortunately, based on what you see on social media (including Substack Notes).Data visualization is far more useful than simply placing bar charts on an executive dashboard.I’ve been doing analytics for a long time. Yes, I’ve built executive dashboards. But I overwhelmingly use visualizations to analyze data - not to report on the past.For example, when I train my corporate clients to use machine learning, the first hands-on lab has attendees build data visualizat
A Guide to My Analytics Tutorials (So Far) (opens the original)
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When I started The DIY Data Scientist six months ago, I never expected it to become as popular as it has. I sincerely hope this is because so many professionals have found my analytics tutorials useful in achieving their career goals.To date, I've published 48 tutorials that include Excel workbooks, data, and code. For new subscribers, navigating all of these tutorials can be intimidating. This article is meant to guide professionals like you through all this content.I’ve organized the tutorial
Forecasting with ARIMA Part 8: Partnering with AI (opens the original)
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This is the 8th in a growing library of AI prompts that accelerate your impact. If you’re new to this tutorial series, check out Part 1 here.While it’s tempting to want to feed your data to AI and trust that the output is correct, this is a bad idea for two reasons:AI tools like ChatGPT, Claude, and Copilot often make mistakes in analytics. For example, they often make incorrect assumptions and never t
Linear Regression the Intuitive Way (opens the original)
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Linear regression is just as useful today as it was when it was invented more than 200 years ago. Skills with linear regression allow you to build explainable predictive models that predict numeric quantities like:Length of employee tenure.Next year’s monthly sales.Customer call volume.As the examples above show, linear regression is a universal technique useful to professionals in any role and industry.In this 2-hour crash course, you will jump-start your skills with linear regression, includin
Forecasting with ARIMA Part 7: Improving Your Model (opens the original)
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Are you new to this tutorial series? Check out Part 1 here.In this tutorial, you will explore how to improve the first iteration of your ARIMA model by enriching the dataset to capture seasonality. As with all predictive modeling tasks, this enrichment (also called feature engineering) isn't guaranteed to work. However, you won’t know until you try.If you would like to follow along with today’s tutorial (highly recommended), you will need to download the SalesTimeSeries.xlsx file from the new
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