Comprehensive Data Analytics curriculum covering the entire data value chain. From data manipulation with Google Sheets and SQL to creating interactive dashboards with Power BI and Looker Studio, through automation with ELT tools (dbt, Zapier) and predictive analysis with Python (Pandas, Scikit-Learn). Learn to identify data sources, build funnels, analyze customer retention, and recommend concrete actions from your analyses.
Data manipulation with Google Sheets and advanced formulas
Advanced SQL with BigQuery: analytical queries, CTEs, window functions
Data modeling for marketing, sales and product teams
ELT tools: dbt for transformation, Zapier for automation
Web tracking with Google Tag Manager and tracking plans
APIs and webhooks for data extraction
Data visualization with Power BI and Looker Studio (Google Data Studio)
Statistical analysis and AB testing
Python for analysis: Pandas, Plotly, Jupyter, Google Colab
Applied Machine Learning: churn prediction, customer segmentation with Scikit-Learn
Analysis methodology: KPIs, sales funnels, retention, cohorts
End-to-end project: from problem identification to automated dashboard
The most important concepts to understand this technology and ace your interviews
Google Sheets: advanced formulas (VLOOKUP, INDEX/MATCH, ARRAYFORMULA), pivot tables, automation
SQL: SELECT, JOIN, GROUP BY, HAVING, window functions (ROW_NUMBER, RANK, LAG/LEAD), CTEs, subqueries
BigQuery: partitioning, clustering, nested queries, cost optimization, UDFs
Data Modeling: star schemas, fact and dimension tables, normalization, denormalization
KPIs & Metrics: CAC, LTV, MRR, ARR, churn rate, NPS, conversion rate, ARPU
Funnels & Cohorts: conversion analysis, cohort retention, RFM analysis
ELT & Data Pipeline: Extract-Load-Transform, dbt (models, tests, sources), orchestration
Zapier & automation: triggers, actions, multi-step workflows, webhooks
Google Tag Manager: tags, triggers, variables, dataLayer, tracking plans
Power BI: DAX, calculated measures, relationships, visualizations, filters, drill-down
Looker Studio: data sources, calculated fields, filters, parameters, blending
Visualization: choosing the right chart, data storytelling, design principles (Tufte)
AB Testing: hypotheses, sample size, statistical significance, p-value, Student's t-test
Python & Pandas: DataFrames, Series, groupby, merge, pivot_table, cleaning
Plotly: interactive charts, subplots, animations, dashboards
Scikit-Learn: regression, classification, clustering (K-Means), train/test split, metrics
Methodology: problem framing, source identification, cleaning, analysis, recommendations
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