This page helps you choose the next step without getting lost in the catalog. Start with the recommended order, then pick a track when you know which direction you want to specialize in.
Recommended order
If you want a linear path, follow these phases from top to bottom. Each row is a module: open it, complete the main lessons, and move to the next phase.
Method, tracking, metrics, and decision operating system before entering specializations.
Transform the course from catalog to cognitive system, with study method, technical memory, and professional output plan.
Move from isolated tags and tools to reliable, governed, and observable measurement systems.
Learn to read numbers as systems, with KPI trees, unit economics, correct baselines, and causal signal interpretation.
Bring data into the everyday way of deciding with cadence, governance, decision memo, and cross-functional alignment.
Product, marketing, dashboards, and decision models applied to real growth problems.
Read where the product creates value, friction, or loss with funnel, retention, activation, and monetization analytics.
Take marketing from reporting to a serious quantitative discipline with incrementality, unit economics, and decision reviews.
Build dashboards that drive decisions, with visual hierarchy, semantic consistency, alerting, and operational context.
Introduces business-useful models, distinguishing prediction, causation, scenario planning, and model risk.
Warehouse, lakehouse, streaming, realtime, and operational discipline for reliable analytical systems.
Truly understand how to design a scalable analytical system with correct grain, historization, and architectural trade-offs.
Move from buckets with files to a readable, governed data lake compatible with modern lakehouse patterns.
Teach data logic as a flow, with schema contracts, stream processing, and realistic failure handling.
Turn realtime from slogan to robust system with ingestion patterns, MergeTree thinking, and cost control.
Bring operational discipline from serious engineering to data workloads, deploy, observability, security, and incident response.
Advanced SQL, mathematics, analytics engineering, and experiment science to raise the course level.
Use SQL as a reasoning language, not just extraction, with patterns for cohorts, funnels, experiments, and performance.
Introduces the mathematical rigor that makes models, inference, forecasting, and applied regressions more readable.
Transform raw data into reliable, reusable, and governed models with layering, tests, contracts, and semantic layer.
Take experimentation to a serious level with causal design, power planning, variance reduction, and governance.
Roles, economic responsibilities, causality, applied AI, and critical data reading in complex decision-making contexts.
Connect technical modules to real roles, economic responsibilities, and professional specialization choices.
Give epistemological, causal, and critical depth to data work, linking evidence, models, risk, and responsibility.
Use generative AI and AutoML to accelerate analysis, pipelines, features, and decision memos without losing control, quality, and ownership.
Design agentic workflows for analysis, SQL, pipelines, AutoML, and analytics operations with tools, state, guardrails, evaluation, and approval.
Specialized tracks
Tracks do not replace the recommended order: they help filter the path when you want to reach a role or a type of project.
Track
Final track for those who want to govern measurement architecture, incrementality, budget allocation, and growth-oriented decisions.
Track
Final track for those who want to work on product analytics, retention, experimentation science, and applied causality.
Track
Final track for those who want to design the end-to-end analytics stack, from tracking to warehouse to serving layer.
Final capstone
The capstone is the final project: it combines tracking, KPIs, data architecture, dashboards, experiments, and business recommendations into a single presentable case.
5
milestone
8
deliverable
Open it only when you want to search for modules, see prerequisites, bridging lessons, and topic connections.