Start Here: Non-Brand Data
A structured way to build practical ML, GenAI, and analytics judgment without learning randomly.
Hi there, Cornellius here!👋
Non-Brand Data helps working data professionals build practical judgment in ML, analytics, and GenAI through structured essays, field guides, reusable templates, and applied workflows.
Do this in order (5 steps)
Start with the Focus Map (Free PDF)
Download it and pick one track:
SQL & Analytics: for decision-making and business analysis
Python + ML: for practical modeling, baselines, and evaluation
GenAI / RAG: for workflows, retrieval, evaluation, and AI systems
Commit to a 2–4 week sprint (do not switch halfway)
Schedule three sessions per week.
Each session is ~60 minutes:
10 min read
35 min practice (SQL query/notebook / small build)
15 min write a short note (“what I learned + what I’ll do next”)
Ship one visible artifact every week
Pick one format:
a note (Notion/Doc), or
a notebook, or
a GitHub repo, or
a short write-up.
Finish the mini project at the end of the track
Your goal is not “understanding.” Your goal is a finished artefact you can point to.After you ship, tighten it into portfolio-ready quality
Use the Portfolio Rubric Toolkit to score and improve your project in 30–60 minutes:
Quick links
I am new and want structure
I have a project and want to improve it
I am a paid member and want templates
I am a paid member and feel lost in the archive
I want to know what free and paid subscribers get
What is Non-Brand Data?
Non-Brand Data is a practical learning publication for data professionals who want to build stronger judgment in ML, analytics, and Generative AI.
What you’ll get from Non-Brand Data
Practical judgment: what matters in real data work, including tools, trade-offs, workflows, and failure modes.
Structured learning paths: how to move from scattered reading to focused practice.
Reusable assets: templates, checklists, rubrics, and mini-project structures you can apply to your own work.
Applied examples: practical breakdowns across SQL, ML, GenAI, RAG, evaluation, and portfolio projects.
If you follow the Focus Map cadence, you will finish each track with something you can reuse: a notebook, repo, or write-up.
In 2026, Non-Brand Data is focused on practical judgment in modern data work:
The ML skills that still matter,
Practical GenAI workflows,
AI evaluation and output reliability,
Stronger data projects,
and turning learning into useful work.
This is slightly tighter.
Want an Example?
If you want to sample before committing, start with these:
SQL: #1 What is SQL?
Python + ML: Use Scikit-Learn Like a Pro
GenAI / RAG: Simple RAG Implementation with Contextual Semantic Search
Portfolio: 14 Portfolio Projects That Demonstrate Real Business Value
Who writes Non-Brand Data?
I’m Cornellius Yudha Wijaya, a data professional with 8+ years of experience across data science, machine learning, analytics, and AI work.
I write Non-Brand Data from the perspective of someone who has worked on real data projects, managed data/AI work, taught technical topics, written about data science professionally, and built practical learning resources for data professionals.
The goal is to make data and AI learning more structured, practical, and connected to real work.
Enough about me, let’s hear about you!
What has been your aspiration in the world of data science and AI?
What has brought you here? What challenges are you facing?
I’d love to hear your story! Just hit reply and share it with me.
I genuinely read and respond to all my emails. I’m delighted you subscribed, and I aim to surpass your expectations in supporting your data career.
So I can’t wait to chat with you.
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