Data Analyst Interview Questions Italy 2026: SQL, Python and Analytics
Prepare for data analyst interviews in Italy with 30 essential questions covering SQL, Python, and business analytics. Includes expected answers for 2026 job market.
Data Analytics
DATA
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.
What you'll learn
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
Key topics to master
The most important concepts to understand this technology and ace your interviews
1
Google Sheets: advanced formulas (VLOOKUP, INDEX/MATCH, ARRAYFORMULA), pivot tables, automation
2
SQL: SELECT, JOIN, GROUP BY, HAVING, window functions (ROW_NUMBER, RANK, LAG/LEAD), CTEs, subqueries
3
BigQuery: partitioning, clustering, nested queries, cost optimization, UDFs
4
Data Modeling: star schemas, fact and dimension tables, normalization, denormalization
5
KPIs & Metrics: CAC, LTV, MRR, ARR, churn rate, NPS, conversion rate, ARPU
6
Funnels & Cohorts: conversion analysis, cohort retention, RFM analysis
7
ELT & Data Pipeline: Extract-Load-Transform, dbt (models, tests, sources), orchestration
8
Zapier & automation: triggers, actions, multi-step workflows, webhooks
9
Google Tag Manager: tags, triggers, variables, dataLayer, tracking plans
10
Power BI: DAX, calculated measures, relationships, visualizations, filters, drill-down
11
Looker Studio: data sources, calculated fields, filters, parameters, blending
12
Visualization: choosing the right chart, data storytelling, design principles (Tufte)
13
AB Testing: hypotheses, sample size, statistical significance, p-value, Student's t-test
14
Python & Pandas: DataFrames, Series, groupby, merge, pivot_table, cleaning
15
Plotly: interactive charts, subplots, animations, dashboards
16
Scikit-Learn: regression, classification, clustering (K-Means), train/test split, metrics
17
Methodology: problem framing, source identification, cleaning, analysis, recommendations
Today's Data Analytics challenge
One snippet a day. Find the bug, or prove there isn't one.
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