Data processing and analysis covers the day-to-day work of turning raw operational data into something a person or model can act on. The category mixes statistics, programming, and data engineering: building pipelines that move data between systems, cleaning and reshaping it, exploring patterns, and producing summaries or features for downstream models and dashboards.
The toolchain has consolidated significantly in the last five years. Python with pandas or polars handles in-memory analysis on a laptop. DuckDB runs analytical SQL on local files faster than most data warehouses. For pipelines: dbt for warehouse transforms, Airflow or Dagster for orchestration. For dashboards: Metabase, Superset, or Tableau on top of a Postgres / Snowflake / BigQuery warehouse.
What you'll work with in these 74 courses
- SQL fundamentals: window functions, CTEs, query planning
- Python data libraries: pandas, polars, numpy, DuckDB, pyarrow
- Data cleaning: handling missing values, outliers, schema drift
- Visualization: matplotlib, seaborn, plotly, Looker Studio
- Pipelines and orchestration: dbt, Airflow, Dagster, Prefect
- Statistics: hypothesis tests, A/B testing, confidence intervals
The skill set applies whether you're tracking conversion funnels in a SaaS product, building a recommendation system at an e-commerce company, doing financial modeling at a bank, or producing public-health dashboards.
