Data Science
Kanalga Telegram’da o‘tish
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases
Ko'proq ko'rsatish📈 Telegram kanali Data Science analitikasi
Data Science (@sql_databases) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 70 778 obunachidan iborat bo'lib, Taʼlim toifasida 2 259-o'rinni va Hindiston mintaqasida 4 564-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 70 778 obunachiga ega bo‘ldi.
27 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -317 ga, so‘nggi 24 soatda esa -15 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 11.42% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.47% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 8 084 marta ko‘riladi; birinchi sutkada odatda 1 749 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 0 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent database, learning, linkedin, udemy, 029k| kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 28 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
70 778
Obunachilar
-1524 soatlar
-1477 kunlar
-31730 kunlar
Postlar arxiv
70 779
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.
70 779
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
70 779
📂 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.
70 779
🔅 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
70 779
🔅 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
70 779
🔅 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
70 779
Which python library is not used specifically for data visualization?
