Endi mavjud! Telegram Tadqiqoti 2025 โ yilning asosiy insaytlari 
Data Science & Machine Learning
Kanalga Telegramโda oโtish
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Ko'proq ko'rsatish๐ Telegram kanali Data Science & Machine Learning analitikasi
Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 831 obunachidan iborat bo'lib, Taสผlim toifasida 2 106-o'rinni va Hindiston mintaqasida 4 234-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 75 831 obunachiga ega boโldi.
21 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 770 ga, soโnggi 24 soatda esa 8 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oโrtacha 3.15% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.09% ini tashkil etuvchi reaksiyalarni toโplaydi.
- Post qamrovi: Har bir post oโrtacha 2 385 marta koโriladi; birinchi sutkada odatda 827 ta koโrish yigโiladi.
- Reaksiyalar va oโzaro taโsir: Auditoriya faol: har bir postga oโrtacha 3 ta reaksiya keladi.
- Tematik yoโnalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.
๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
โJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataโ
Yuqori yangilanish chastotasi (oxirgi maโlumot 22 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taสผlim toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
75 831
Obunachilar
+824 soatlar
+717 kunlar
+77030 kunlar
Postlar arxiv
DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)
๐ Power BI:
Q1: Explain step-by-step how you will create a sales dashboard from scratch.
Q2: Explain how you can optimize a slow Power BI report.
Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.
๐SQL:
Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.
Q2 โ Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)
Q2: Find the nth highest salary from the Employee table.
Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.
Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.
Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)
๐Behavioral:
Q1: Why do you want to become a data analyst and why did you apply to this company?
Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?
I have curated best top-notch Data Analytics Resources ๐๐
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Hope this helps you ๐
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SQL JOINS ๐๏ธ๐
๐ SQL JOINS are used to combine data from multiple tables.
๐น 1. Why JOINS are Needed?
In real databases, data is stored in different tables.
Example:
Employees Table
emp_id: 1
name: Rahul
Salary Table
emp_id: 1
salary: 50000
๐ To combine employee name with salary โ use JOIN.
๐ฅ 2. INNER JOIN โญ
Returns only matching rows from both tables.
SELECT employees.name, salary.salary
FROM employees
INNER JOIN salary
ON employees.emp_id = salary.emp_id;
SELECT *
FROM employees
LEFT JOIN salary
ON employees.emp_id = salary.emp_id;
SELECT *
FROM employees
RIGHT JOIN salary
ON employees.emp_id = salary.emp_id;
SELECT *
FROM employees
FULL OUTER JOIN salary
ON employees.emp_id = salary.emp_id;
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SQL for Data Science ๐๏ธ๐
๐ SQL is one of the most important skills for Data Scientists and Data Analysts.
Almost every company stores data inside databases, and SQL helps retrieve and analyze that data.
๐น 1. What is SQL?
SQL = Structured Query Language
๐ Used to:
โ Store data
โ Retrieve data
โ Filter data
โ Analyze data
๐ฅ 2. Common Database Systems
โ MySQL
โ PostgreSQL
โ SQLite
โ Microsoft SQL Server
๐น 3. Basic SQL Query
โ
SELECT Statement
Used to retrieve data from a table.
SELECT * FROM employees;
๐ ** means all columns.
๐น 4. Select Specific Columns
SELECT name, salary FROM employees;
๐น 5. WHERE Clause โญ
Used for filtering data.
SELECT * FROM employees
WHERE salary > 50000;
๐น 6. ORDER BY
Sort data.
SELECT * FROM employees
ORDER BY salary DESC;
โ ASC โ Ascending
โ DESC โ Descending
๐น 7. Aggregate Functions โญ
Used for calculations.
Function: COUNT()
Purpose: Count rows
Function: SUM()
Purpose: Total
Function: AVG()
Purpose: Average
Function: MAX()
Purpose: Highest value
Function: MIN()
Purpose: Lowest value
โ
Example
SELECT AVG(salary)
FROM employees;
๐น 8. GROUP BY โญ
Used to group data.
SELECT department, AVG(salary)
FROM employees
GROUP BY department;
๐น 9. Why SQL is Important?
โ Most asked interview skill
โ Used daily by analysts & data scientists
โ Essential for working with databases
๐ฏ Todayโs Goal
โ Learn SELECT queries
โ Filter using WHERE
โ Use aggregate functions
โ Understand GROUP BY
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End-to-End Machine Learning Project Workflow ๐ค๐
๐ Today youโll learn how real-world ML projects are built from start to finish.
This is one of the most important topics for interviews and projects.
๐น 1. Problem Understanding
๐ First understand the business problem.
Example:
โ Predict house prices
โ Detect spam emails
โ Customer churn prediction
๐ฅ 2. Collect Data
Data can come from:
โ CSV files
โ APIs
โ Databases
โ Web scraping
๐น 3. Data Cleaning
Clean messy data:
โ Handle missing values
โ Remove duplicates
โ Fix data types
โ Handle outliers
Using:
Pandas
๐น 4. Exploratory Data Analysis (EDA)
Understand the dataset:
โ Trends
โ Patterns
โ Correlations
โ Distributions
Using:
Matplotlib & Seaborn
๐น 5. Feature Engineering โญ
Create useful features for better prediction.
Examples:
โ Extract month from date
โ Convert categories into numbers
โ Create new calculated columns
๐น 6. Split Data
Train Data โ Learn patterns
Test Data โ Evaluate model
Usually:
โ 80% Training
โ 20% Testing
๐ฅ 7. Train Machine Learning Model
Choose algorithm:
โ Linear Regression
โ Random Forest
โ SVM
โ KNN
๐น 8. Evaluate Model
Check performance using:
โ Accuracy
โ Precision
โ Recall
โ RMSE
๐น 9. Hyperparameter Tuning
Improve model using:
โ Grid Search
โ Cross Validation
๐น 10. Deploy Model โญ
Make model usable in real world.
Tools:
โ Flask
โ Streamlit
โ FastAPI
๐น 11. Monitor Model
After deployment:
โ Track performance
โ Retrain if needed
๐ฅ 12. Real-World Workflow Summary
Problem โ Data โ Cleaning โ EDA โ
Feature Engineering โ Model โ
Evaluation โ Deployment
๐ฏ Todayโs Goal
โ Understand full ML lifecycle
โ Learn project workflow
โ Understand deployment basics
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Data Analyst vs Data Scientist vs Business Analyst vs ML Engineer vs Gen AI Engineer
Which of the following is a hyperparameter in KNN?
Which method is commonly used for Hyperparameter Tuning?
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Cross Validation & Hyperparameter Tuning ๐คโ๏ธ
๐ Building a model is not enough.
We must also make sure it performs well on unseen data.
This is done using:
โ Cross Validation
โ Hyperparameter Tuning
๐น 1. What is Cross Validation?
Cross Validation checks how well a model generalizes to new data.
๐ Instead of using only one train-test split, data is divided multiple times.
๐ฅ 2. K-Fold Cross Validation โญ
How it Works:
1๏ธโฃ Split data into K parts (folds)
2๏ธโฃ Use one fold for testing
3๏ธโฃ Use remaining folds for training
4๏ธโฃ Repeat until every fold is tested
โ
Example
If K = 5:
โข 4 folds โ Training
โข 1 fold โ Testing
Repeated 5 times.
๐น 3. Why Cross Validation is Important?
โ Better model evaluation
โ Reduces overfitting risk
โ More reliable accuracy
๐น 4. Implementation (Python)
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
scores = cross_val_score(model, X, y, cv=5)
print(scores)
from sklearn.model_selection import GridSearchCV
params = {
"n_neighbors": [3,5,7]
}
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Which of the following can help reduce overfitting?
