Статистически анализ и тестване на хипотези в Python със SciPy и Statsmodels
Научете как да провеждате t-тестове, ANOVA, хи-квадрат и регресия в Python със SciPy и Statsmodels — практическо ръководство с работещи примери и визуализации за 2026 г.
Daniel is a staff data engineer with 13 years across fintech and logistics. He spent four years at Plaid building the transaction-enrichment pipeline (Python + Kafka + Snowflake), three years before that at Flexport on the freight-visibility data platform, and started his career at IBM doing DB2 performance work he still grudgingly draws on. He writes about the gluework of modern Python data stacks: Prefect 2 flow design, dbt run orchestration from Python, Pydantic-based contract validation between Bronze and Silver layers, and the operational realities of running polars in containers with strict memory limits. He has contributed patches to dbt-core and to the prefect-snowflake integration. Daniel is based in Lagos and Lisbon depending on the quarter, holds AWS Solutions Architect Professional, and writes a small newsletter about data-platform postmortems.
Научете как да провеждате t-тестове, ANOVA, хи-квадрат и регресия в Python със SciPy и Statsmodels — практическо ръководство с работещи примери и визуализации за 2026 г.
Научете как да визуализирате данни с Matplotlib и Seaborn в Python. Примери с код за линейни графики, scatter plots, heatmaps, box plots, pair plots и дашборди. Актуално за 2026 г.