The DIY Data Scientist

Cadence

Weekly

Latest

2026-10-28

By

David Langer

Based on

12 recent issues

• Covers: Practical tutorials on Python programming, customer lifetime value calculations, machine learning models, and forecasting techniques for workplace analytics.
• For: Professionals seeking practical, hands-on data analytics and Python skills for work.
• Best for: Professionals wanting to learn applied analytics and data science to improve job performance.
• Skip if: Experienced data scientists seeking advanced theoretical research or purely non-technical readers.

Start with

• Python Crash Course Part 1: Understanding Data Types: Explains foundational Python data types needed to analyze tables, clusters, and machine learning models.
• Customer Lifetime Value Part 5: Predicting CLV: Shows how to predict customer lifetime value using RFM segmentation and machine learning models.
• Random Forest Machine Learning: Teaches the random forest machine learning algorithm in an intuitive, approachable format for professionals.

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