Data Analyst learning path: everything to study, topic by topic

Core SQL querying: SELECT columns, filtering with WHERE, joining tables, and aggregating data with GROUP BY and HAVING

Advanced SQL: window functions (ROW_NUMBER, RANK, LAG, LEAD, running totals) and Common Table Expressions for readable, modular queries

Correlated and uncorrelated subqueries, EXISTS, IN, UNION, INTERSECT, EXCEPT, and choosing between subqueries and joins

Reading EXPLAIN plans, understanding indexes, avoiding common performance pitfalls like SELECT *, N+1 queries, and missing WHERE clauses on large tables

Central tendency (mean, median, mode), dispersion (variance, standard deviation, IQR), distributions, and summary statistics for data exploration

Null and alternative hypotheses, p-values, confidence intervals, t-tests, chi-square tests, and statistical significance

Pearson and Spearman correlation, simple and multiple linear regression, R-squared, and the difference between correlation and causation

Designing A/B experiments, sample size calculations, interpreting results, common pitfalls (peeking, multiple comparisons), and Bayesian vs frequentist approaches

Building cohort retention tables, funnel conversion analysis, churn analysis, and LTV estimation from behavioral data

Applying advanced statistical inference techniques: Bayesian inference, bootstrapping, permutation tests, and power analysis

Designing statistically rigorous experiments: power calculations, randomization, guardrail metrics, and sequential testing

Using causal inference methods: difference-in-differences, instrumental variables, regression discontinuity, and DAGs

Analyzing temporal data: trend identification, seasonality detection, decomposition methods, moving averages, and basic forecasting techniques

Designing effective questionnaires, Likert scale analysis, sampling strategies, response bias mitigation, and statistical validity of survey results

Choosing the right chart type for different data types and questions: bar, line, scatter, pie, histogram, box plot, heatmap

Layout hierarchy, cognitive load, information density, color usage, filter design, and responsive dashboards

Narrative structure for data presentations, annotation techniques, context framing, and audience-appropriate communication

Building interactive dashboards with BI tools: calculated fields, parameters, actions, LOD expressions, DAX, and LookML basics

Creating C-suite reports: KPI scorecards, trend summaries, exception-based reporting, and strategic recommendations

Small multiples, sparklines, animated visualizations, interactive dashboards with drill-through, custom chart types, and data art for engagement

Advanced Excel/Sheets: VLOOKUP/XLOOKUP, pivot tables, conditional formatting, array formulas, and data validation

DataFrame operations, groupby, merge, pivot, vectorized operations with NumPy, and method chaining for data analysis workflows

Handling missing values, duplicates, type conversions, string normalization, date parsing, and data quality validation

Consuming REST APIs, parsing JSON responses, pagination handling, rate limiting, and flattening nested JSON into tabular format

Scheduling reports with cron/Airflow, templated email reports, Jupyter notebook automation, and data freshness monitoring

Building financial models in spreadsheets, advanced pivot table techniques, INDEX-MATCH combinations, dynamic arrays, macros/VBA basics, and scenario analysis

Understanding upstream data pipelines, data freshness concepts, lineage tracking, SLA monitoring, and collaborating effectively with data engineers

Defining meaningful KPIs, setting targets, building tracking dashboards, and distinguishing vanity metrics from actionable metrics

Presenting findings to non-technical audiences, managing expectations, handling pushback on data, and translating business questions into analytical tasks

Eliciting analytical requirements from stakeholders, scoping projects, defining success criteria, and managing ambiguity

Applying metrics frameworks like North Star Metric, AARRR pirate metrics, OKRs, and balanced scorecards to structure analytical work

Structuring decisions with data: cost-benefit analysis, decision matrices, expected value calculations, and communicating uncertainty

Identifying and mitigating bias in datasets, ethical data reporting, privacy considerations, misrepresentation risks, and responsible analytics practices

Funnel analysis, user retention curves, feature adoption metrics, user journey mapping, activation metrics, and product-led growth analytics

Unit economics analysis, LTV/CAC calculations, revenue analytics, cohort-based financial modeling, and building financial dashboards

Building simple predictive models: logistic regression, decision trees, feature selection, train/test splits, and model evaluation metrics

RFM analysis, k-means clustering, behavioral segmentation, and translating segments into business actions

Attribution models (first-touch, last-touch, linear, time-decay, data-driven), multi-touch attribution, and incrementality testing

Designing self-service analytics platforms: semantic layers, data catalogs, governed datasets, training programs, and balancing autonomy with governance

Applying NLP techniques to business analytics: sentiment analysis, topic modeling, text classification, and embeddings

Applying analytics engineering practices: semantic layer, metrics store, data contracts, and self-serve analytics design

Working with location data: spatial joins, heatmaps, distance calculations, choropleth maps, geocoding, and GIS fundamentals for business analytics

Building analytics tools for non-technical users, documentation best practices, training material creation, dashboard handoff, and adoption measurement