Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Artificial Intelligence & ChatGPT Prompts
Kanalga Telegram’da o‘tish
🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data
Ko'proq ko'rsatish📈 Telegram kanali Artificial Intelligence & ChatGPT Prompts analitikasi
Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 42 114 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 229-o'rinni va Hindiston mintaqasida 9 545-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 42 114 obunachiga ega bo‘ldi.
12 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 175 ga, so‘nggi 24 soatda esa 12 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.43% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.73% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 024 marta ko‘riladi; birinchi sutkada odatda 306 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, algorithm, detection, llm, pattern kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“🔓Unlock Your Coding Potential with ChatGPT
🚀 Your Ultimate Guide to Ace Coding Interviews!
💻 Coding tips, practice questions, and expert advice to land your dream tech job.
For Promotions: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 13 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
42 114
Obunachilar
+1224 soatlar
+227 kunlar
+17530 kunlar
Postlar arxiv
SQL Basics for Data Analysts
SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.
1️⃣ Understanding Databases & Tables
Databases store structured data in tables.
Tables contain rows (records) and columns (fields).
Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).
2️⃣ Basic SQL Commands
Let's start with some fundamental queries:
🔹 SELECT – Retrieve Data
SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees SELECT DISTINCT department FROM employees; -- Show unique departments 
𝗧𝗼𝗽 𝟱 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Learning JavaScript doesn’t have to be boring anymore!💫
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Perfect for daily practice, weekend sprints, or anyone who learns better with hands-on interaction!9✅️
SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.
Let’s dive into a comprehensive guide on SQL for Data Science!
I have broken it down into three key sections to help you:
𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.
𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.
𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.
𝗧𝗲𝗰𝗵 𝗝𝗼𝗯𝘀 𝗜𝗻 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 | Across India😍
Companies Hiring:- Google, Microsoft, Cognizant, Infosys, TCS & Many More
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Future-Proof Skills for Data Analysts in 2025 & Beyond
1️⃣ AI-Powered Analytics 🤖 Leverage AI and AutoML tools like ChatGPT, DataRobot, and H2O.ai to automate insights and decision-making.
2️⃣ Generative AI for Data Analysis 🧠 Use AI for generating SQL queries, writing Python scripts, and automating data storytelling.
3️⃣ Real-Time Data Processing ⚡ Learn streaming technologies like Apache Kafka and Apache Flink for real-time analytics.
4️⃣ DataOps & MLOps 🔄 Understand how to deploy and maintain machine learning models and analytical workflows in production environments.
5️⃣ Knowledge of Graph Databases 📊 Work with Neo4j and Amazon Neptune to analyze relationships in complex datasets.
6️⃣ Advanced Data Privacy & Ethics 🔐 Stay updated on GDPR, CCPA, and AI ethics to ensure responsible data handling.
7️⃣ No-Code & Low-Code Analytics 🛠️ Use platforms like Alteryx, Knime, and Google AutoML for rapid prototyping and automation.
8️⃣ API & Web Scraping Skills 🌍 Extract real-time data using APIs and web scraping tools like BeautifulSoup and Selenium.
9️⃣ Cross-Disciplinary Collaboration 🤝 Work with product managers, engineers, and business leaders to drive data-driven strategies.
🔟 Continuous Learning & Adaptability 🚀 Stay ahead by learning new technologies, attending conferences, and networking with industry experts.
Like for detailed explanation ❤️
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Repost from SQL Programming Resources
𝟰 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗦𝗤𝗟 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍
Want to break into Data Analytics?💫
It all starts with SQL — the language every data analyst needs to master. Whether you’re analyzing trends, pulling business reports, or cleaning datasets, SQL is at the heart of it all👨💻📌
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Perfect for students, freshers, job seekers, or anyone transitioning into tech✅️
If you want to Excel in Data Science and become an expert, master these essential concepts:
Core Data Science Skills:
• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends
Machine Learning (ML):
• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search
Deep Learning (DL):
• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs
Real-World Applications:
• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making
React ❤️ for more
𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 𝗜𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱😍
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📊 Data Analyst Roadmap (2025)
Master the Skills That Top Companies Are Hiring For!
📍 1. Learn Excel / Google Sheets
Basic formulas & formatting
VLOOKUP, Pivot Tables, Charts
Data cleaning & conditional formatting
📍 2. Master SQL
SELECT, WHERE, ORDER BY
JOINs (INNER, LEFT, RIGHT)
GROUP BY, HAVING, LIMIT
Subqueries, CTEs, Window Functions
📍 3. Learn Data Visualization Tools
Power BI / Tableau (choose one)
Charts, filters, slicers
Dashboards & storytelling
📍 4. Get Comfortable with Statistics
Mean, Median, Mode, Std Dev
Probability basics
A/B Testing, Hypothesis Testing
Correlation & Regression
📍 5. Learn Python for Data Analysis (Optional but Powerful)
Pandas & NumPy for data handling
Seaborn, Matplotlib for visuals
Jupyter Notebooks for analysis
📍 6. Data Cleaning & Wrangling
Handle missing values
Fix data types, remove duplicates
Text processing & date formatting
📍 7. Understand Business Metrics
KPIs: Revenue, Churn, CAC, LTV
Think like a business analyst
Deliver actionable insights
📍 8. Communication & Storytelling
Present insights with clarity
Simplify complex data
Speak the language of stakeholders
📍 9. Version Control (Git & GitHub)
Track your projects
Build a data portfolio
Collaborate with the community
📍 10. Interview & Resume Preparation
Excel, SQL, case-based questions
Mock interviews + real projects
Resume with measurable achievements
✨ React ❤️ for more
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Q1: How would you analyze data to understand user connection patterns on a professional network?
Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.
Q2: Describe a challenging data visualization you created to represent user engagement metrics.
Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.
Q3: How would you identify and target passive job seekers on LinkedIn?
Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.
Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?
Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.
𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍
Dreaming of a career in Data Analytics but don’t know where to begin?
The Career Essentials in Data Analysis program by Microsoft and LinkedIn is a 100% FREE learning path designed to equip you with real-world skills and industry-recognized certification.
𝐋𝐢𝐧𝐤👇:-
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𝟏𝟎𝟎+ 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍
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Data Analytics Interview Questions
Q1: Describe a situation where you had to clean a messy dataset. What steps did you take?
Ans: I encountered a dataset with missing values, duplicates, and inconsistent formats. I used Python's Pandas library to identify and handle missing values, standardized data formats using regular expressions, and removed duplicates. I also validated the cleaned data against known benchmarks to ensure accuracy.
Q2: How do you handle outliers in a dataset?
Ans: I start by visualizing the data using box plots or scatter plots to identify potential outliers. Then, depending on the nature of the data and the problem context, I might cap the outliers, transform the data, or even remove them if they're due to errors.
Q3: How would you use data to suggest optimal pricing strategies to Airbnb hosts?
Ans: I'd analyze factors like location, property type, amenities, local events, and historical booking rates. Using regression analysis, I'd model the relationship between these factors and pricing to suggest an optimal price range. Additionally, analyzing competitor pricing in the area can provide insights into market rates.
Q4: Describe a situation where you used data to improve the user experience on the Airbnb platform.
Ans: While analyzing user feedback and platform interaction data, I noticed that users often had difficulty navigating the booking process. Based on this, I suggested streamlining the booking steps and providing clearer instructions. A/B testing confirmed that these changes led to a higher conversion rate and improved user feedback.
𝟰 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 — 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 & 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱!😍
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𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:
df = pd.read_csv('your_dataset.csv')
𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
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JavaScript (JS) roadmap:
1. Basic Fundamentals:
- Variables, data types, and operators.
- Control structures like loops and conditionals.
- Functions and scope.
2. DOM Manipulation:
- Access and modify HTML and CSS using JavaScript.
- Event handling.
3. Asynchronous Programming:
- Promises and async/await for handling asynchronous operations.
4. ES6 and Modern JavaScript:
- Arrow functions, template literals, and destructuring.
- Modules for code organization.
- Classes for object-oriented programming.
5. Popular Libraries and Frameworks:
- Learn libraries like jQuery or frameworks like React, Angular, or Vue depending on your project needs.
6. Package Management:
- Tools like npm or yarn for managing dependencies.
7. Build Tools:
- Webpack, Babel, and other tools for bundling and transpiling.
8. API Interaction:
- Fetch or Axios for making API requests.
9. State Management (For Frameworks):
- Redux for React, Vuex for Vue, etc.
10. Testing:
- Learn testing frameworks like Jest.
11. Version Control:
- Git for code versioning and collaboration.
12. Continuous Integration (CI) and Deployment:
- Travis CI, Jenkins, or others for automating testing and deployment.
13. Server-Side JavaScript (Optional):
- Node.js for server-side development.
14. Advanced Topics (Optional):
- WebSockets, WebRTC, Progressive Web Apps (PWAs), and more.
This roadmap covers the foundational knowledge and key steps in a JavaScript developer's journey. You can explore more deeply into areas that align with your specific goals and projects.
