Zero-fluff, step-by-step master tracks designed for business analysts, everyday teams, and AI engineers. Progress at your own pace from scratch to production.
Showing all 51 series across 5 tracks
🎓 Core Analytics & Data Skills
The foundational curriculum: go from messy spreadsheets to confident SQL, Python, charts, and metrics you reuse everywhere.
★ Flagship Core Curriculum
SQL series
This path is for you if a spreadsheet is the wrong place for the question. The numbered tutorials start with a playground and the core commands, then reading, filtering, grouping, and joins, then subqueries, changes, views, and faster queries. One extra primer asks what SQL is. Start with the first part on this page, and do not skip ahead to joins.
Key Milestones:
- Parts 1–4: Playground setup, SELECT, filtering, and sorting
- Parts 5–8: Grouping, aggregates, multi-table joins & subqueries
- Parts 9–14: Views, indexes, window functions, CTEs & query tuning
Start Part 1 (14 Lessons) →
SELECT department,
COUNT(order_id) AS volume,
ROUND(AVG(revenue), 2) AS ticket
FROM warehouse.orders
WHERE status = 'shipped'
GROUP BY 1
HAVING volume > 1000;
🤖 Everyday AI for Work & Life
Practical AI workflows for knowledge workers — prompting, memory, personal assistants, and real work tasks.
★ Flagship Workplace AI
Practical AI for analytics people
This path is for analytics people who will use AI on a real file, not a demo. Parts cover what it can and cannot do, tokens and cost as a feel, prompts for data work, checking an answer, retrieval in plain English, agents, what you should not paste, documentation, and a checklist you can reuse. Start with the first part on this page.
Key Milestones:
- Parts 1–3: Capabilities, limits, token economics & prompt framing
- Parts 4–6: RAG foundations, agents, evals & structured data extraction
- Parts 7–9: Privacy boundaries, governance checklists & production ops
Start Part 1 (9 Lessons) →
// Workplace AI Loop
Input: messy business brief
Step 1: Extract constraints & entities
Step 2: Compare model reasoning traces
Step 3: Verify outputs with facts
Result: Decisions shipped 5x faster
🛠️ Major AI Platform Masterclasses
Deep-dive operational playbooks for frontier models and developer ecosystems with official marks.
★ Frontier Model Masterclass
Claude
This path is for you if Claude is the tool on your screen and the product names do not line up. Early parts are the plain guide, plans, a first half hour, projects and files, writing, and what never to paste. Later parts split into the product map, Claude Code, and Cowork, including when each one is the wrong tool. Start with the first part on this page.
Key Milestones:
- Parts 1–3: Prompt caching, Projects, Artifacts & context hygiene
- Parts 4–6: Claude Code CLI tool execution, subagents & workflows
- Parts 7–10: MCP server integration, tool boundaries & developer best practices
Start Part 1 (31 Lessons) →
$ claude
> Analyze Q3 churn data and propose 3 SQL fixes
Thinking Process (Extended Thinking)…
[Tool Call] read_file query.sql
[Result] 3 queries optimized
Ready for review.
🧠 Open-Source & AI Engineering
Run, build, fine-tune, and inspect models locally — llama.cpp, Unsloth, RAG, vector databases, and autonomous harnesses.
★ Deep AI Engineering Path
Fine-tuning open models with Unsloth from scratch
This path is for you if you want to change an open model's weights, not only write a better prompt. The tool is Unsloth, and the split is a prompt versus a weight update. No part is live on this page yet. Start with Meta Llama from scratch if you still need the map of the model, and come back here for the training path.
Key Milestones:
- Part 1: Weight updates vs RAG & prompt engineering trade-offs
- Part 2: 5x faster custom CUDA autograd kernels with 80% less VRAM
- Part 3: LoRA / QLoRA training on Colab GPUs & GGUF production export
Start Part 1 (3 Lessons) →
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = 'unsloth/Meta-Llama-3.1-8B',
max_seq_length = 2048,
load_in_4bit = True,
)
# 5x faster, 80% less memory
🏢 Data Systems, Engineering & Applied Practice
How data moves, scales, and delivers value: pipelines, dbt, Airflow, stewardship, BI, and domain analytics.
★ Production Systems Anchor
How data actually moves
This path is for analysts who inherited a pipeline and were never in the room when it was designed. Parts walk the path from sources to a table someone queries, batch versus streaming, where the data lives, orchestration, what dbt is for, dev versus prod, and how you notice a break. Start with the first part on this page.
Key Milestones:
- Parts 1–2: Trace data movement, lineage & latency requirements
- Parts 3–5: Storage tiers, orchestration engines & dbt transformations
- Parts 6–7: Multi-environment deployments & proactive observability alerts
Start Part 1 (7 Lessons) →
Source: CRM & Event Streams
↓ (Extract & Load: 15m cadence)
Raw Warehouse Landing
↓ (dbt Transformations & Tests)
Curated Dimensional Marts
↓ (Power BI / Metric APIs)
Executive Dashboards (99.9% SLA)
