Where the numbers end,vision begins
Marketing and Data Engineering in the same path.
Practical lessons, born from real problems and tools used in the field.
Marketing Data & Analytics Engineering
Parti dalle basi e costruisci un percorso pratico: tracking, KPI, SQL, dashboard, esperimenti, AI e infrastruttura dati. Ogni modulo risponde a un problema reale.
Course Overview and Study Method for Data Work
Transform the course from catalog to cognitive system, with study method, technical memory, and professional output plan.
Data Collection & Tracking Systems
Move from isolated tags and tools to reliable, governed, and observable measurement systems.
Metrics, KPI Trees, and Analytical Foundations
Learn to read numbers as systems, with KPI trees, unit economics, correct baselines, and causal signal interpretation.
Data-Driven Management and Decision Operating System
Bring data into the everyday way of deciding with cadence, governance, decision memo, and cross-functional alignment.
Product Analytics and Growth Diagnostics
Read where the product creates value, friction, or loss with funnel, retention, activation, and monetization analytics.
Marketing Analytics, Incrementality, and Unit Economics
Take marketing from reporting to a serious quantitative discipline with incrementality, unit economics, and decision reviews.
Dashboard, Visualization, and Decision Interface
Build dashboards that drive decisions, with visual hierarchy, semantic consistency, alerting, and operational context.
Marketing Data Science, Forecasting, and Decision Models
Introduces business-useful models, distinguishing prediction, causation, scenario planning, and model risk.
Data Warehousing & Analytical Architecture
Truly understand how to design a scalable analytical system with correct grain, historization, and architectural trade-offs.
S3, Data Lake, and Lakehouse Architecture
Move from buckets with files to a readable, governed data lake compatible with modern lakehouse patterns.
Kafka & Event Streaming Engineering
Teach data logic as a flow, with schema contracts, stream processing, and realistic failure handling.
Real-Time Analytics & ClickHouse Systems
Turn realtime from slogan to robust system with ingestion patterns, MergeTree thinking, and cost control.
Infrastructure & Ops for Data Systems
Bring operational discipline from serious engineering to data workloads, deploy, observability, security, and incident response.
Advanced SQL for Analytical Systems
Use SQL as a reasoning language, not just extraction, with patterns for cohorts, funnels, experiments, and performance.
Mathematics for Data Analysis
Introduces the mathematical rigor that makes models, inference, forecasting, and applied regressions more readable.
Analytics Engineering with dbt and Semantic Layer
Transform raw data into reliable, reusable, and governed models with layering, tests, contracts, and semantic layer.
Statistical Significance, A/B Testing, and Experimentation Science
Take experimentation to a serious level with causal design, power planning, variance reduction, and governance.
Directions in Analytics: Marketing, Product, Finance
Connect technical modules to real roles, economic responsibilities, and professional specialization choices.
Philosophical Foundations of Data Analysis
Give epistemological, causal, and critical depth to data work, linking evidence, models, risk, and responsibility.
AI for Data Analysis, Data Engineering and AutoML
Use generative AI and AutoML to accelerate analysis, pipelines, features, and decision memos without losing control, quality, and ownership.
Agentic AI for Data Analysis, Data Engineering, and AutoML
Design agentic workflows for analysis, SQL, pipelines, AutoML, and analytics operations with tools, state, guardrails, evaluation, and approval.