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
Show more📈 Analytical overview of Telegram channel Data Science
Channel Data Science (@sql_databases) in the English language segment is an active participant. Currently, the community unites 70 790 subscribers, ranking 2 261 in the Education category and 4 562 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 70 790 subscribers.
According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -310 over the last 30 days and by -15 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 11.21%. Within the first 24 hours after publication, content typically collects 2.74% reactions from the total number of subscribers.
- Post reach: On average, each post receives 7 934 views. Within the first day, a publication typically gains 1 943 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as database, learning, linkedin, udemy, 029k|.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Thanks to the high frequency of updates (latest data received on 27 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
70 790
Subscribers
-1524 hours
-1277 days
-31030 days
Posts Archive
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?
