Data Science
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Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases
Mostrar más📈 Análisis del canal de Telegram Data Science
El canal Data Science (@sql_databases) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 70 790 suscriptores, ocupando la posición 2 261 en la categoría Educación y el puesto 4 562 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 70 790 suscriptores.
Según los últimos datos del 26 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -310, y en las últimas 24 horas de -15, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 11.21%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.74% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 7 934 visualizaciones. En el primer día suele acumular 1 943 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
- Intereses temáticos: El contenido se centra en temas clave como database, learning, linkedin, udemy, 029k|.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 27 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
70 790
Suscriptores
-1524 horas
-1277 días
-31030 días
Archivo de publicaciones
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Data Science Interview Questions
Question 1 : How would you approach building a recommendation system for personalized content on Facebook? Consider factors like scalability and user privacy.
- Answer: Building a recommendation system for personalized content on Facebook would involve collaborative filtering or content-based methods. Scalability can be achieved using distributed computing, and user privacy can be preserved through techniques like federated learning.
Question 2 : Describe a situation where you had to navigate conflicting opinions within your team. How did you facilitate resolution and maintain team cohesion?
- Answer: In navigating conflicting opinions within a team, I facilitated resolution through open communication, active listening, and finding common ground. Prioritizing team cohesion was key to achieving consensus.
Question 3 : How would you enhance the security of user data on Facebook, considering the evolving landscape of cybersecurity threats?
- Answer: Enhancing the security of user data on Facebook involves implementing robust encryption mechanisms, access controls, and regular security audits. Ensuring compliance with privacy regulations and proactive threat monitoring are essential.
Question 4 : Design a real-time notification system for Facebook, ensuring timely delivery of notifications to users across various platforms.
- Answer: Designing a real-time notification system for Facebook requires technologies like WebSocket for real-time communication and push notifications. Ensuring scalability and reliability through distributed systems is crucial for timely delivery.
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How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗️ Bayes' Theorem & conditional probability
↗️ Permutations & combinations
↗️ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗️ Mean, median, mode
↗️ Standard deviation and variance
↗️ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗️ A/B experimentation
↗️ T-test, Z-test, Chi-squared tests
↗️ Type 1 & 2 errors
↗️ Sampling techniques & biases
↗️ Confidence intervals & p-values
↗️ Central Limit Theorem
↗️ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗️ Logistic & Linear regression
↗️ Decision trees & random forests
↗️ Clustering models
↗️ Feature engineering
↗️ Feature selection methods
↗️ Model testing & validation
↗️ Time series analysis
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📂 Full description
Are you familiar with SQL? Do you know Python? Are you interested in understanding how these two languages work together? Then join Bill Weinman in this course as he shows the power of these two languages combined. Bill starts with some basics—connecting to a database, performing simple queries, and reading rows from a table. He covers how to use prepared statements and cursors, how to build a wrapper class to streamline the SQL interface and support multiple different database engines, and how to build a CRUD class and a full-featured web application using what you've learned. Many applications require a combination of SQL and Python, and after finishing Bills course, youll have a better understanding of why and how you can leverage the power of these two languages together.
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🔅 Using SQL with Python
🌐 Author: Bill Weinman
🔰 Level: Intermediate
⏰ Duration: 1h 39m
🌀 If you already know SQL and Python, learn the power of using these two languages together.📗 Topics: SQL, Python 📤 Join Data Analysis and Databases for more courses
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🔅 Machine Learning and AI Foundations: Classification Modeling
🌐 Author: Keith McCormick
🔰 Level: Intermediate
⏰ Duration: 2h 5m
🌀 Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.📗 Topics: Machine Learning, Artificial Intelligence, Data Classification 📤 Join Data Analysis and Databases for more courses
70 779
🔅 Learning Digital Business Analysis
🌐 Author: Angela Wick
🔰 Level: Intermediate
⏰ Duration: 1h 26m
🌀 Discover how new digital technologies are changing traditional business models and processes. Learn what these changes mean and how to implement technologies and changes.📗 Topics: Business Analysis 📤 Join Data Analysis and Databases for more courses
70 779
📱Data Analysis and Databases
📱Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
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🔅 Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
🌐 Author: Gwendolyn Stripling
🔰 Level: Intermediate
⏰ Duration: 1h 56m
🌀 Learn the knowledge and practical skills needed to effectively utilize deep learning techniques using the Python programming language.📗 Topics: Generative AI, Deep Learning, Python 📤 Join Data Analysis and Databases for more courses
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Which python library is not used specifically for data visualization?
