Hypnos

Dark Souls es tan...

Cats 🧵 1.

Hace un rato una costurera ha comentado en TVE que ha perdido su taller de costura en Paiporta (Valencia) tras la DANA, al rato una costurera de Murcia que se acaba de jubilar ha llamado para donarle su taller.

@CallejasGR80 @TwitchSupportES Este mensaje lo mandas tú o es un bot automatizado?? Vaya tela!!!!! 🤦🤦🤦🤦🤦🤦

ok @TwitchSupportES 3 dias sin hacer streaming...

SORTEO - 2 Libros de Los Secretos de las Tierras Intermedias firmados - RT a este post - Seguir a @nuevebits - Solo ES. Ganadores el 14/06 Muchas gracias por todo el cariño que le dais y le habéis dado a este libro :)

📚Mis recomendaciones para dominar el arte del #DataViz con libros/recursos online y gratuitos.📊💡 ✨Crear visualizaciones efectivas es clave para comunicar datos de manera impactante y comprensible. 📖¿Cuál es tu libro favorito sobre el tema? ¡Compártelo! #DataScience #Rstats

@Truetoneti Dimisión ya!!!

La rueda de prensa del hijo de puta de Vizcaino .... Nadie salvo tus varrigs agradecidas estan encantados con la gestión del club. Vete perro!

GitHub repository with everything you need to become proficient in #PyTorch, with 15 implemented projects: github.com/Coder-World04/… — compiled by @NainaChaturved8 ➕ See this book: amzn.to/3eC3x2p —— #DataScience #DataScientists #AI #MachineLearning #Python #DeepLearning

Excel for Data Science Complete Study Plan 🚀 The timeline of 30 days and you have to dedicate at least 1 hour a day. Week 1: Basics of Excel Day 1-2: Excel Basics youtube.com/watch?v=c_ZJLJ… Day 3-4: Understanding Formulas, Functions and Formatting youtube.com/watch?v=PNnvuA… Day 5-7: Practice Basic Operations Practice: Start by working on simple datasets, performing basic arithmetic operations, and using functions. Datasets: kaggle.com/datasets?searc… Week 2: Data Manipulation Day 8-10: Sorting, Filtering, and Data Validation Sorting and Filtering: youtube.com/watch?v=O28-xL… Data Validation: youtube.com/watch?v=SlWIgM… Day 11-12: PivotTables Resource: youtube.com/watch?v=lH7Hfw… Day 13-14: Practice Data Manipulation Practice: Work with moderately complex datasets, practice sorting, filtering, and creating PivotTables. Example: youtube.com/watch?v=_g5roK… Week 3: Advanced Functions and Analysis Day 15-20: Advanced Functions Resource: youtube.com/watch?v=F264Fp… Day 21-22: Statistical Functions and Analysis Resource: youtube.com/watch?v=3F_V5a… Week 4: Visualization and Presentation Day 23-25: Charts and Graphs youtube.com/watch?v=eHtZrI… Day 26-28: Advanced Visualizationhttps://www.youtube.com/watch?v=HrqgBhZDWUg&list=PLDO6p2ilGI3EkDJZtQqi3VUdnIhW6lQUu Day 29-30: Final Projects and RecapPractice: Apply learned skills to create comprehensive visual presentations using Excel. Example Project: youtube.com/watch?v=opJgMj… Datasets: kaggle.com/datasets?searc… Throughout the Month: Review and Practice: Regularly revisit previous topics, practice, and work on sample datasets to reinforce your learning.

👀Si te perdiste (como yo) las Charlas y Talleres de posit::conf(2023), ya puedes verlas online y aprender de: 👉Pruebas A/B 👉Mantenimiento confiable de modelos de#ML 👉Conformal Inference con Tidymodels ¡Y más! posit.co/blog/modeling-… #RStats #DataScience #stats #analytics

Data warehouse schema designs In most transactional databases that are used, the data is normalized to reduce duplication. In a data warehouse, however, the dimension data is generally de-normalized to reduce the number of joins required to query the data. Often, a data warehouse is organized as a star schema. The fact table is directly related to the dimension tables, as shown in this example: The attributes of an entity can be used to aggregate measures in fact tables over multiple hierarchical levels. For example, to find total sales revenue by country or region, city, postal code, or individual customer. The attributes for each level can be stored in the same dimension table. When an entity has a large number of hierarchical attribute levels, or when some attributes can be shared by multiple dimensions. For example, both customers and stores have a geographical address. It can make sense to apply some normalization to the dimension tables and create a snowflake schema, as shown in the following example: In this case, the DimProduct table has been normalized to create separate dimension tables for product categories and suppliers. A DimGeography table has been added to represent geographical attributes for both customers and stores. Each row in the DimProduct table contains key values for the corresponding rows in the DimCategory and DimSupplier tables. Each row in the DimCustomer and DimStore tables contains a key value for the corresponding row in the DimGeography table. --- That's it! Leave a Comment a comment of you think!

13 Hard Truths of Psychology & Life: 1.

La Tontunada Star Wars del día...

"Go out and read some ML papers." That's the most harmful advice for beginners. They will understand nothing and fall into depression. Those papers are the tip of the iceberg. What they need is deep in the sea. Start low and build up!

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Por favor y gracias 😅

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Quick learner.. 😊 twitter.com/buitengebieden…

Everything You Can Do in Excel