Data Analysis Podcast Summaries
Data Analysis on Yedapo: 9 summarized podcast and YouTube episodes. Each includes key takeaways, core concepts and notable quotes with timestamps.
Python for Engineers & Robotics – Master NumPy, Pandas, and ChatGPT Automation
freeCodeCamp.org
Aug 11, 2026
Python has become an indispensable asset for mechanical engineers, enabling the automation of tedious calculations, complex data analysis, and simulation workflows. By leveraging libraries like NumPy, Pandas, and Matplotlib, engineers can shift their focus from manual computation to solving genuine design and thermal management challenges, significantly increasing their overall efficiency.
Key insight: Mechanical engineers can reduce the time spent on mathematical and thermodynamic problem-solving by up to 40% by automating workflows with Python, allowing them to focus on higher-level engineering decisions.
Can You Solve These Advanced PostgreSQL Queries? 🔥 Joins, GROUP BY, HAVING
Thapa Technical
Jun 21, 2026
This tutorial demonstrates how to perform complex multi-table joins in PostgreSQL to solve real-world business problems. By connecting Courses, Users, and Enrollments tables, you can isolate high-value metrics like premium course popularity and departmental cost averages, moving beyond simple queries to actionable database analysis.
Key insight: When using aggregate functions alongside non-aggregated columns in a SELECT statement, you must include the non-aggregated columns in the GROUP BY clause to ensure accurate data grouping.
Advanced PostgreSQL Practice Session | Real SQL Interview Questions & Solutions
Thapa Technical
Jun 19, 2026
This session bridges the gap between basic SQL theory and complex enterprise requirements. It demonstrates how to combine multiple Joins, Aggregate functions, Grouping, and complex filtering via Having clauses into a single, high-level query to solve real business problems like identifying top-spending students.
Key insight: When building complex business queries, filtering before aggregation requires the 'WHERE' clause, while filtering on aggregate results (like a specific total spent amount) must be performed using the 'HAVING' clause after the 'GROUP BY' statement.
PostgreSQL GROUP BY & HAVING Clause Explained with Practical Questions
Thapa Technical
Jun 18, 2026
The instructor clarifies the functional divide between 'Group By' and 'Having' in PostgreSQL. He explains that 'Group By' is used to organize data into aggregate sets, while 'Having' acts as a secondary filter specifically for those aggregated results. Adhering to the logical query execution sequence—Select, From, Where, Group By, Having—is critical to avoiding syntax errors.
Key insight: The golden rule of SQL: Any column listed in a SELECT statement that is not inside an aggregate function must be included in the GROUP BY clause to prevent execution errors.
PostgreSQL Aggregate Functions Tutorial | COUNT, SUM, AVG, MIN, MAX with Real Examples
Thapa Technical
Jun 15, 2026
एग्रीगेट फंक्शन्स जैसे COUNT, SUM, और AVG किसी भी कॉलम के विशाल डेटा को प्रोसेस कर केवल एक समराइज्ड आउटपुट देते हैं। यह ट्यूटोरियल इन फंक्शन्स के सटीक सिंटैक्स, एलीयासेस के महत्व, और नल वैल्यूज को संभालने की बेस्ट प्रैक्टिसेस को स्पष्ट करता है, जो डेटा एनालिटिक्स में सटीकता के लिए अनिवार्य है।
Key insight: COUNT(*) और COUNT(column) के बीच का अंतर यह है कि स्टार नल वैल्यूज को भी शामिल करता है, जबकि स्पेसिफिक कॉलम का उपयोग करने पर नल वैल्यूज को एक्सक्लूड कर दिया जाता है।
Temporal Networks, Where Page Rank meets Lord of the Rings - Computerphile
Computerphile
Mar 19, 2026
Traditional network analysis treats connections as permanent, missing the critical context of timing. By applying temporal graph theory—where links are annotated with timestamps—researchers can trace the flow of assets or information through time-respecting paths. This approach exposes hidden patterns like crypto-fraud or market manipulation that remain invisible in static, time-agnostic models.
Key insight: Temporal network analysis can identify 'wash trading' in crypto or NFT markets by revealing when assets are traded in circular patterns between a small group of wallets to artificially inflate prices.
¿Nivel Avanzado? El Reto de BUSCARX que el 90% NO logra resolver 🧠
Sergio Bazo | Herramientas Digitales
Mar 6, 2026
Este episodio desafía a los usuarios de Excel a resolver un complejo cálculo de comisiones cruzadas usando funciones anidadas. Revela cómo combinar SI.CONJUNTO y BUSCARX para gestionar múltiples variables y rangos dinámicos en una base de datos de ventas masiva.
Key insight: La capacidad de anidar la función BUSCARX dentro de SI.CONJUNTO para realizar búsquedas matriciales precisas sin recurrir a macros complejas.
10 Ejercicios Pro de Excel: Funciones Estadísticas y Anidación (Nivel Intermedio 2026)
Sergio Bazo | Herramientas Digitales
Feb 20, 2026
Eleva tu productividad financiera aprendiendo a anidar funciones avanzadas de Excel como 'REDONDEAR.MAS' o 'REDONDEAR.PAR'. Esta guía práctica elimina la navegación entre hojas mediante la nomenclatura de rangos, convirtiendo datos brutos en insights financieros precisos y profesionales.
Key insight: Puedes nombrar columnas completas basándote en encabezados mediante la función 'Crear desde la selección', eliminando la necesidad de referencias de celdas fijas entre hojas.
Tu PRIMERA MEDIDA DAX: el paso exacto para dejar de ser principiante en Power Pivot
Sergio Bazo | Herramientas Digitales
Dec 4, 2025
El análisis de datos profesional requiere abandonar las fórmulas tradicionales de celda en favor de DAX en Power Pivot. Estas medidas dinámicas ofrecen una flexibilidad analítica superior al responder al contexto de los datos en tiempo real.
Key insight: A diferencia de las fórmulas de Excel, las medidas DAX no viven en celdas estáticas; se calculan instantáneamente según el contexto de filtrado del informe.