How to Become a Data Analyst

SELECT, WHERE, GROUP BY, JOIN. The bread and butter — must be second nature.

• tutorial free SQLBolt →
• tutorial free Mode SQL Tutorial →
• book free Select Star SQL →

Window functions, CTEs, subqueries, self-joins. The difference between a junior and senior analyst.

• tutorial free Mode — Window Functions →
• docs free PostgreSQL Window Functions →

Reading EXPLAIN plans, indexes, avoiding cartesian joins. Enough to not lock the warehouse.

• book free Use The Index, Luke! →

Pivot tables, VLOOKUP/XLOOKUP, INDEX/MATCH, arrays, dynamic ranges. Still the daily interface for most business users.

• docs free Exceljet →
• docs free Google Sheets Function List →

The enterprise-default BI tool. Calculated fields, LOD expressions, dashboard actions.

• docs free Tableau Help →
• tutorial free Tableau Public Gallery →

Microsoft-shop default. DAX language, Power Query, data modelling.

• course free Microsoft Learn — Power BI →

Modern BI options. Metabase for the SMB tier, Looker/LookML for governed enterprise, Superset for open source.

• docs free Metabase Docs →
• docs free Looker Docs →

Mean vs median, variance, percentiles, distributions. The base layer for any interpretation.

• book free Think Stats (Downey) →
• tutorial free Seeing Theory (Brown) →

P-values, confidence intervals, sample size. Enough to not overclaim from a small experiment.

• blog free Amplitude — A/B Testing →

Fact vs dimension, star vs snowflake schema. Even if you don't build them, understanding models saves hours of guessing.

• docs free Kimball Techniques →
• book free The Analytics Setup Guidebook →

SQL-native modelling tool. Analyst-engineer bridge role increasingly requires this.

• course free dbt Learn →
• docs free dbt Docs →

For analyses that outgrow SQL. pandas, matplotlib, seaborn. Notebook fluency.

• docs free pandas Getting Started →
• book free Python Data Science Handbook →

Preferred in stats-heavy fields (biotech, econ, academic). ggplot is still the gold standard for statistical viz.

• book free R for Data Science (Wickham) →
• book free ggplot2: Elegant Graphics for Data Analysis →

Bars vs lines vs scatter — the choice reveals or hides the pattern. Tufte, Wilke, Cairo as reference.

• book free Fundamentals of Data Visualization (Wilke) →
• blog free Storytelling with Data →

One question per dashboard, hierarchy of information, actionable defaults. Less is almost always more.

• blog free NN/g — Dashboards →

Executives don't read dashboards. They read a headline, one chart, and one recommendation.

• blog free Storytelling with Data (Cole Knaflic) →
• blog free Benn Stancil's Substack →

Product analytics, marketing analytics, finance analytics, ops analytics. Depth in one beats surface breadth in five.

• blog free Amplitude Blog — Product Analytics →
• blog free Locally Optimistic →

For dbt projects, notebooks, collaborative SQL work. Enough git and PR fluency to work with engineering.

• tutorial free Learn Git Branching →

Where working analysts talk shop.

• blog free Locally Optimistic (data leaders) →
• blog free Benn Stancil →