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
Step-by-step SQL — from playground setup through joins, subqueries, and optimization.
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
A nine-part series for analysts: what AI can and cannot do, tokens, prompts, evals, RAG, agents, privacy, documentation, and a personal ops checklist.
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
Anthropic Claude from first login through product map, Claude Code, and Cowork — one continuous path.
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
A comprehensive, zero-fluff engineering guide to fine-tuning open foundation models using Unsloth. Understand the mechanics of weight updates versus prompt engineering and RAG, master manual CUDA autograd kernels that deliver 5x training speedups with 80% lower VRAM, explore LoRA and QLoRA low-rank adapter mathematics, prepare high-quality Alpaca and ShareGPT instruction datasets, fine-tune Llama 3 and Qwen models on free Google Colab GPUs, and export merged adapters directly to GGUF, Ollama, and vLLM for high-throughput production serving.
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
A seven-part series for analysts who inherit pipelines: path, latency, storage homes, orchestration, dbt concepts, environments, and observability.
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)
