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Network:📈 Analytical overview of Telegram channel Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Channel Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) in the English language segment is an active participant. Currently, the community unites 39 619 subscribers, ranking 4 707 in the Education category and 9 752 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 39 619 subscribers.
According to the latest data from 26 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -40 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.28%. Within the first 24 hours after publication, content typically collects 0.69% reactions from the total number of subscribers.
- Post reach: On average, each post receives 903 views. Within the first day, a publication typically gains 274 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as analytic, dataset, visualization, sql, learning.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former.
Ads/ Promo: @love_data”
Thanks to the high frequency of updates (latest data received on 27 October, 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.
39 619
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Posts Archive
🚀 Complete Data Analyst Roadmap 📊🔥
🧠 STEP 1: Learn Spreadsheet Basics
✔ Data Entry & Cleaning
✔ Formulas & Functions
✔ Sorting & Filtering
✔ Charts & Dashboards
🛠 Tools to Learn:
✔ Microsoft Excel
✔ Google Sheets
📊 STEP 2: Master SQL
✔ SELECT & WHERE
✔ JOINS & GROUP BY
✔ Window Functions
✔ CTEs & Subqueries
✔ Query Optimization
🛠 Databases to Learn:
✔ MySQL
✔ PostgreSQL
✔ SQL Server
🐍 STEP 3: Learn Python for Data Analysis
✔ Data Cleaning
✔ Data Analysis
✔ Automation
✔ Visualization
🛠 Libraries to Learn:
✔ Pandas
✔ NumPy
✔ Matplotlib
✔ Seaborn
📈 STEP 4: Learn Data Visualization
✔ Interactive Dashboards
✔ KPIs & Metrics
✔ Data Storytelling
✔ Business Insights
🛠 Tools to Learn:
✔ Power BI
✔ Tableau
📊 STEP 5: Learn Statistics Basics
✔ Mean, Median & Mode
✔ Probability Basics
✔ Correlation
✔ Hypothesis Testing
✔ A/B Testing
☁️ STEP 6: Learn Business & Domain Knowledge
✔ Business Metrics
✔ Customer Analytics
✔ Sales Analytics
✔ Financial Reporting
✔ KPI Analysis
🔄 STEP 7: Learn Data Cleaning & ETL
✔ Handling Missing Data
✔ Removing Duplicates
✔ Data Transformation
✔ Data Validation
🛠 Tools to Learn:
✔ Power Query
✔ Alteryx
🔥 STEP 8: Build Real Projects
✔ Sales Dashboard
✔ HR Analytics Dashboard
✔ Customer Churn Analysis
✔ Financial Analytics Report
✔ Netflix Data Analysis Project
💡 The best way to become a Data Analyst:
👉 Learn SQL → Analyze Data → Create Dashboards → Build Projects
💬 Tap ❤️ if this helped you!
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🔥 Top SQL Projects for Data Analytics 🚀
If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!
Here are some must-do SQL projects to strengthen your portfolio. 👇
🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)
✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨
🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)
⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬
🔵 Advanced SQL Projects (For Business-Level Analytics)
🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prescriptions
🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)
🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prescription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️
🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!
React with ♥️ if you want detailed explanation of each project
Share with credits: 👇 https://t.me/sqlspecialist
Hope it helps :)
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Data Analyst Interview Preparation Roadmap ✅
Technical skills to revise
- SQL
Write queries from scratch.
Practice joins, group by, subqueries.
Handle duplicates and NULLs.
Window functions basics.
- Excel
Pivot tables without help.
XLOOKUP and IF confidently.
Data cleaning steps.
- Power BI or Tableau
Explain data model.
Write basic DAX.
Explain one dashboard end to end.
- Statistics
Mean vs median.
Standard deviation meaning.
Correlation vs causation.
- Python. If required
Pandas basics.
Groupby and filtering.
Interview question types
- SQL questions
Top N per group.
Running totals.
Duplicate records.
Date based queries.
- Business case questions
Why did sales drop.
Which metric matters most and why.
- Dashboard questions
Explain one KPI.
How users will use this report.
- Project questions
Data source.
Cleaning logic.
Key insight.
Business action.
Resume preparation
- Must have Tools section.
- One strong project.
- Metrics driven points.
Example: Improved reporting time by 30 percent using Power BI.
Mock interviews
- Practice explaining out loud.
- Time your answers.
- Use real datasets.
Daily prep plan
1 SQL problem.
1 dashboard review.
10 interview questions.
- Common mistakes
Memorizing queries.
No project explanation.
Weak business reasoning.
- Final task
- Prepare one project story.
- Prepare one SQL solution on paper.
- Prepare one business metric explanation.
Double Tap ♥️ For More
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+7
Data Analyst Roadmap
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🔥 Don't wait—start learning today and unlock better career opportunities!
✅ Complete Roadmap to Learn SQL (Structured Query Language) 🧠💻
Week 1: SQL Basics
• What is SQL and how databases work
• Install MySQL Workbench or PostgreSQL
• Learn SELECT, FROM, WHERE
• Filtering data with conditions
• Practice basic queries
Example: Fetch all employees, filter salary > 50k
Week 2: Sorting and Aggregation
• ORDER BY (sorting data)
• Aggregate functions: COUNT, SUM, AVG, MIN, MAX
• GROUP BY concept
• HAVING clause
Example: Department-wise average salary
Week 3: Joins (Most Important 🔥)
• INNER JOIN
• LEFT JOIN, RIGHT JOIN
• FULL JOIN
• Self Join
Example: Combine employees and departments tables
Week 4: Advanced Filtering
• IN, BETWEEN, LIKE
• Wildcards (% , _)
• NULL handling (IS NULL, IS NOT NULL)
• CASE statements
Example: Categorize customers based on spending
Week 5: Subqueries
• Nested queries
• Correlated subqueries
• Using subqueries in SELECT, WHERE
Example: Find employees earning above average salary
Week 6: Window Functions (High Value 💰)
• OVER() clause
• ROW_NUMBER(), RANK(), DENSE_RANK()
• PARTITION BY
Example: Rank employees by salary within each department
Week 7: CTE & Views
• Common Table Expressions (WITH)
• Temporary vs permanent views
• Simplify complex queries
Example: Multi-step data transformation
Week 8: Data Modification
• INSERT, UPDATE, DELETE
• TRUNCATE vs DELETE
• Constraints (PRIMARY KEY, FOREIGN KEY)
Example: Update employee salary
Week 9: Indexing & Performance
• What are indexes
• Query optimization basics
• EXPLAIN keyword
Example: Speed up large table queries
Week 10: Working with Real Data
• Import CSV data
• Data cleaning in SQL
• Handling duplicates
• Basic transformations
Example: Clean messy sales dataset
Week 11: Mini Projects
• Write complex queries
• Solve real-world case studies
• Focus on business logic
Examples: Sales dashboard queries, Customer segmentation
Week 12: Final Preparation
• Revise all concepts
• Practice interview questions
• Solve SQL challenges on LeetCode / HackerRank
• Mock interviews
Daily Rule for You
• Practice SQL 60 minutes daily
• Solve 5 queries daily
• Revise previous queries weekly
🔥 Pro Tip
• Focus more on JOINS + WINDOW FUNCTIONS
• Practice real datasets, not just theory
• Think in terms of “business questions”
Double Tap ❤️ For Detailed Explanation
-----
2.06 ₽ · /balance_help
📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 🚀
Company Name :- Collegedunia
✅ Role: Data Analyst Intern
📍 Location: Gurugram, Haryana
🏢 Work Mode: On-site
👩💻 Experience: Freshers / Students
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⏳ Apply Before the link expires!
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𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐯𝐬. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 – 𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞?
Whether you're starting a career in data or looking to pivot, it’s crucial to understand the key differences between a Data Analyst and a Data Scientist:
👓 𝐅𝐨𝐜𝐮𝐬
Data Analyst: Interprets existing data to uncover insights.
Data Scientist: Predicts future trends using advanced models.
🛠️ 𝐓𝐨𝐨𝐥𝐬 𝐔𝐬𝐞𝐝
Data Analyst: Excel, SQL, Tableau
Data Scientist: Python, R, Machine Learning tools
💼 𝐓𝐲𝐩𝐞 𝐨𝐟 𝐖𝐨𝐫𝐤
Data Analyst: Reporting and dashboarding
Data Scientist: Building models and algorithms
🧠 𝐒𝐤𝐢𝐥𝐥𝐬𝐞𝐭
Data Analyst: Data cleaning, visualization
Data Scientist: Data-driven product development and strategy
Both roles are essential—but they serve different purposes. One tells you what happened, the other helps you decide what to do next.
Last 25 seats | Batch closing this week!
𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 (𝗡𝗼 𝗖𝗼𝗱𝗶𝗻𝗴 𝗡𝗲𝗲𝗱𝗲𝗱)
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Complete Data Science Roadmap
👇👇
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Like if you need similar content 😄👍
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