اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
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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
نمایش بیشتر📈 تحلیل کانال تلگرام Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
کانال Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 39 526 مشترک است و جایگاه 4 689 را در دسته آموزش و رتبه 9 978 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 39 526 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 25 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 127 و در ۲۴ ساعت گذشته برابر 1 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.13% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.56% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 842 بازدید دریافت میکند. در اولین روز معمولاً 220 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 2 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند analytic, dataset, visualization, sql, learning تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“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”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 26 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کردهاند.
39 526
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در حال بارگیری داده...
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پستهای کانال
✅ End to End Data Analytics Project Roadmap
Step 1. Define the business problem
Start with a clear question.
Example: Why did sales drop last quarter?
Decide success metric.
Example: Revenue, growth rate.
Step 2. Understand the data
Identify data sources.
Example: Sales table, customers table.
Check rows, columns, data types.
Spot missing values.
Step 3. Clean the data
Remove duplicates.
Handle missing values.
Fix data types.
Standardize text.
Tools: Excel or Power Query SQL for large datasets.
Step 4. Explore the data
Basic summaries.
Trends over time.
Top and bottom performers.
Examples: Monthly sales trend, top 10 products, region-wise revenue.
Step 5. Analyze and find insights
Compare periods.
Segment data.
Identify drivers.
Examples: Sales drop in one region, high churn in one customer segment.
Step 6. Create visuals and dashboard
KPIs on top.
Trends in middle.
Breakdown charts below.
Tools: Power BI or Tableau.
Step 7. Interpret results
What changed?
Why it changed?
Business impact.
Step 8. Give recommendations
Actionable steps.
Example: Increase ads in high margin regions.
Step 9. Validate and iterate
Cross-check numbers.
Ask stakeholder questions.
Step 10. Present clearly
One-page summary.
Simple language.
Focus on impact.
Sample project ideas
• Sales performance analysis.
• Customer churn analysis.
• Marketing campaign analysis.
• HR attrition dashboard.
Mini task
• Choose one project idea.
• Write the business question.
• List 3 metrics you will track.
Example: For Sales Performance Analysis
Business Question: Why did sales drop last quarter?
Metrics:
1. Revenue growth rate
2. Sales target achievement (%)
3. Customer acquisition cost (CAC)
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| 4 | 🚨 SQL Fact Most Beginners Learn Too Late!
Many aspiring Data Analysts assume these two queries do the same thing... but they don't. 👇
📌 COUNT(*)
✅ Counts all rows in the table.
📌 COUNT(column_name)
✅ Counts only non-NULL values in that column.
Example:
Salary Values:
50000
NULL
70000
🔹 COUNT(*) → 3
🔹 COUNT(Salary) → 2
💡 This small difference can completely change your analysis and is a favorite topic in SQL interviews.
❤️ React with a ❤️ if you learned something new today, and follow for more SQL, Excel, Power BI & Data Analyst interview tips! | 549 |
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| 6 | Scenario based Interview Questions & Answers for Data Analyst
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
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| 8 | ✅ Top Data Analyst Projects That Impress Recruiters 📈💼
1. Sales Data Analysis
→ Analyze monthly/quarterly sales trends
→ Segment by product, region, and sales reps
→ Tools: Excel, SQL, Power BI/Tableau
2. Customer Retention Dashboard
→ Churn analysis and retention KPIs
→ Use cohort analysis, funnel visualization
→ Tools: Python, Tableau
3. E-commerce Data Exploration
→ Study user behavior, conversion rate
→ Analyze cart abandonment, top-selling products
→ Tools: SQL, Python (Pandas, Matplotlib)
4. HR Data Insights
→ Track hiring trends, attrition, diversity metrics
→ Build dashboards showing tenure, department stats
→ Tools: Excel, Power BI
5. Financial Data Modeling
→ Actual vs. forecasted revenue/costs
→ Include profitability ratios and variance analysis
→ Tools: Excel, Power BI, SQL
6. Web Traffic Analysis
→ Analyze Google Analytics or log data
→ Focus on user paths, bounce rates, session duration
→ Tools: Python, SQL
7. Survey Data Insights
→ Clean raw survey data, visualize trends
→ Sentiment analysis on feedback (optional NLP)
→ Tools: Excel, Python, Tableau
Tips:
• Explain the business impact of your insights
• Show your workflow: data cleaning → analysis → visualization
• Host projects on GitHub or portfolio site
💬 Tap ❤️ for more! | 577 |
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| 10 | 📂 Top Projects for Data Analytics Portfolio 🚀💻
📊 1. Sales Dashboard (Excel / Power BI / Tableau)
▶️ Analyze monthly/quarterly sales by region, category
▶️ Show KPIs: Revenue, YoY Growth, Profit Margin
🛍 2. E-commerce Customer Segmentation (Python + Clustering)
▶️ Use RFM (Recency, Frequency, Monetary) model
▶️ Visualize clusters with Seaborn / Plotly
📉 3. Churn Prediction Model (Python + ML)
▶️ Dataset: Telecom or SaaS customer data
▶️ Techniques: Logistic Regression, Decision Tree
📦 4. Supply Chain Delay Analysis (SQL + Tableau)
▶️ Identify causes of late deliveries using historical order data
▶️ Visualize supplier-wise performance
📈 5. A/B Testing for Product Feature (SQL + Python)
▶️ Simulate or use real test data (e.g. button click-through rates)
▶️ Metrics: Conversion Rate, Significance Test
📍 6. COVID-19 Trend Tracker (Python + Dash)
▶️ Scrape or pull live data from APIs
▶️ Show cases, recovery, testing rates by country
📅 7. HR Analytics – Attrition Analysis (Excel / Python)
▶️ Predict or explore employee exits
▶️ Use decision trees or visual storytelling
💡 Tip: Upload projects to GitHub + create a simple portfolio site or blog to stand out.
💬 Double Tap ❤️ For More | 707 |
| 11 | ✅ Power BI Roadmap: Step-by-Step Guide to Master Power BI 📊💻
Whether you're aiming to be a data analyst, business intelligence pro, or dashboard expert — this roadmap has you covered 👇
📍 1. Power BI Basics
⦁ Get familiar with Power BI Desktop interface
⦁ Connect to data sources (Excel, CSV, databases)
⦁ Learn Basic visualizations: tables, charts, slicers
📍 2. Data Transformation & Modeling
⦁ Use Power Query Editor to clean & shape data
⦁ Create relationships between tables
⦁ Understand data types & formats
📍 3. DAX Fundamentals
⦁ Master calculated columns & measures
⦁ Learn core functions: SUM, CALCULATE, FILTER, RELATED
⦁ Use variables and time intelligence functions
📍 4. Advanced Visualizations
⦁ Build interactive reports and dashboards
⦁ Use bookmarks, buttons & drill-throughs
⦁ Customize visuals & layouts for storytelling
📍 5. Data Refresh & Gateway
⦁ Set up scheduled refresh with data gateways
⦁ Understand live vs import modes
⦁ Manage refresh performance
📍 6. Row-Level Security (RLS)
⦁ Learn to restrict data access by user roles
⦁ Implement roles & test security in reports
📍 7. Power BI Service & Collaboration
⦁ Publish reports to Power BI Service
⦁ Share dashboards and collaborate with teams
⦁ Use workspaces, apps, and permissions
📍 8. Power BI Mobile & Embedded
⦁ Optimize reports for mobile devices
⦁ Embed Power BI visuals in apps or websites
📍 9. Performance Optimization
⦁ Use Performance Analyzer to tune reports
⦁ Optimize data models & DAX queries
⦁ Best practices for large datasets
📍 10. Power BI API & Automation
⦁ Use Power BI REST API for automation
⦁ Integrate with Power Automate & Azure services
📍 11. Real Projects & Practice
⦁ Build sample dashboards: Sales, Marketing, Finance
⦁ Join challenges on platforms like Enterprise DNA, Radacad
📍 12. Certification & Career Growth
⦁ Prepare for DA-100 / PL-300 certification
⦁ Build portfolio & LinkedIn presence
⦁ Apply for BI Analyst & Power BI Developer roles
💡 Pro Tip: Combine Power BI skills with SQL and Python for a powerful data career combo!
💬 Double Tap ♥️ For More! | 553 |
| 12 |  𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 📊
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🔥 Start your Data Analytics journey today and gain valuable virtual internship experience from a top global company. | 440 |
| 13 | SQL isn't easy!
It’s the powerful language that helps you manage and manipulate data in databases.
To truly master SQL, focus on these key areas:
0. Understanding the Basics: Get comfortable with SQL syntax, data types, and basic queries like SELECT, INSERT, UPDATE, and DELETE.
1. Mastering Data Retrieval: Learn advanced SELECT statements, including JOINs, GROUP BY, HAVING, and subqueries to retrieve complex datasets.
2. Working with Aggregation Functions: Use functions like COUNT(), SUM(), AVG(), MIN(), and MAX() to summarize and analyze data efficiently.
3. Optimizing Queries: Understand how to write efficient queries and use techniques like indexing and query execution plans for performance optimization.
4. Creating and Managing Databases: Master CREATE, ALTER, and DROP commands for building and maintaining database structures.
5. Understanding Constraints and Keys: Learn the importance of primary keys, foreign keys, unique constraints, and indexes for data integrity.
6. Advanced SQL Techniques: Dive into CASE statements, CTEs (Common Table Expressions), window functions, and stored procedures for more powerful querying.
7. Normalizing Data: Understand database normalization principles and how to design databases to avoid redundancy and ensure consistency.
8. Handling Transactions: Learn how to use BEGIN, COMMIT, and ROLLBACK to manage transactions and ensure data integrity.
9. Staying Updated with SQL Trends: The world of databases evolves—stay informed about new SQL functions, database management systems (DBMS), and best practices.
⏳ With practice, hands-on experience, and a thirst for learning, SQL will empower you to unlock the full potential of data!
You can read detailed article here
I've curated essential SQL Interview Resources👇
https://t.me/mysqldata
Share with credits: https://t.me/sqlspecialist
Hope it helps :) | 439 |
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| 15 | Data Analytics Interview Topics in structured way :
🔵Python: Data Structures: Lists, tuples, dictionaries, sets Pandas: Data manipulation (DataFrame operations, merging, reshaping) NumPy: Numeric computing, arrays Visualization: Matplotlib, Seaborn for creating charts
🔵SQL: Basic : SELECT, WHERE, JOIN, GROUP BY, ORDER BY Advanced : Subqueries, nested queries, window functions DBMS: Creating tables, altering schema, indexing Joins: Inner join, outer join, left/right join Data Manipulation: UPDATE, DELETE, INSERT statements Aggregate Functions: SUM, AVG, COUNT, MAX, MIN
🔵Excel: Formulas & Functions: VLOOKUP, HLOOKUP, IF, SUMIF, COUNTIF Data Cleaning: Removing duplicates, handling errors, text-to-columns PivotTables Charts and Graphs What-If Analysis: Scenario Manager, Goal Seek, Solver
🔵Power BI:
Data Modeling: Creating relationships between datasets
Transformation: Cleaning & shaping data using
Power Query Editor Visualization: Creating interactive reports and dashboards
DAX (Data Analysis Expressions): Formulas for calculated columns, measures Publishing and sharing reports, scheduling data refresh
🔵 Statistics Fundamentals: Mean, median, mode Variance, standard deviation Probability distributions Hypothesis testing, p-values, confidence intervals
🔵Data Manipulation and Cleaning: Data preprocessing techniques (handling missing values, outliers), Data normalization and standardization Data transformation Handling categorical data
🔵Data Visualization: Chart types (bar, line, scatter, histogram, boxplot) Data visualization libraries (matplotlib, seaborn, ggplot) Effective data storytelling through visualization
Also showcase these skills using data portfolio if possible
Like for more content like this 😍 | 435 |
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Hurry Up 🏃♂️! Limited seats are available. | 388 |
| 17 | Step-by-step Guide to Create a Data Analyst Portfolio:
✅ 1️⃣ Choose Your Tools & Skills
Decide what tools you want to showcase:
• Excel, SQL, Python (Pandas, NumPy)
• Data visualization (Tableau, Power BI, Matplotlib, Seaborn)
• Basic statistics and data cleaning
✅ 2️⃣ Plan Your Portfolio Structure
Your portfolio should include:
• Home Page – Brief intro about you
• About Me – Skills, tools, background
• Projects – Showcased with explanations and code
• Contact – Email, LinkedIn, GitHub
• Optional: Blog or case studies
✅ 3️⃣ Build Your Portfolio Website or Use Platforms
Options:
• Build your own website with HTML/CSS or React
• Use GitHub Pages, Tableau Public, or LinkedIn articles
• Make sure it’s easy to navigate and mobile-friendly
✅ 4️⃣ Add 3–5 Detailed Projects
Projects should cover:
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Data visualization dashboards or reports
• SQL queries or Python scripts for analysis
Each project should include:
• Problem statement
• Dataset source
• Tools & techniques used
• Key findings & visualizations
• Link to code (GitHub) or live dashboard
✅ 5️⃣ Publish & Share Your Portfolio
Host your portfolio on:
• GitHub Pages
• Tableau Public
• Personal website or blog
✅ 6️⃣ Keep It Updated
• Add new projects regularly
• Improve old ones based on feedback
• Share insights on LinkedIn or data blogs
💡 Pro Tips
• Focus on storytelling with data — explain what the numbers mean
• Use clear visuals and dashboards
• Highlight business impact or insights from your work
• Include a downloadable resume and links to your profiles
🎯 Goal: Anyone visiting your portfolio should quickly understand your data skills, see your problem-solving ability, and know how to reach you.
👍 Tap ❤️ if you found this helpful! | 432 |
| 18 | 🧠 SQL Interview Question (Top-N per Group + Tie Handling - Tricky)
📌
scores(student_id, subject, marks)
❓ Ques :
👉 Find students who scored the highest marks in each subject.
👉 If multiple students have the same top score, include all of them (handle ties).
🧩 How Interviewers Expect You to Think
• Group data by subject 📚
• Identify highest marks within each group
• Handle ties correctly (don’t lose rows)
• Use window functions (RANK vs ROW_NUMBER)
💡 SQL Solution
SELECT
student_id,
subject,
marks
FROM (
SELECT
scores.student_id,
scores.subject,
scores.marks,
RANK() OVER (
PARTITION BY scores.subject
ORDER BY scores.marks DESC
) AS rnk
FROM scores
) ranked_scores
WHERE ranked_scores.rnk = 1;
🔥 Why This Question Is Powerful
• Tests Top-N per group (very common pattern) 🧠
• Checks understanding of RANK vs ROW_NUMBER
❤️ React if you want more real interview-level SQL questions 🚀 | 451 |
| 19 | ✅ SQL Checklist for Data Analysts 🧠💻
📚 1. Understand SQL Basics
☑ What is SQL and how databases work
☑ Relational vs non-relational databases
☑ Table structure: rows, columns, keys
🧩 2. Core SQL Queries
☑ SELECT, FROM, WHERE
☑ ORDER BY, LIMIT
☑ DISTINCT, BETWEEN, IN, LIKE
🔗 3. Master Joins
☑ INNER JOIN
☑ LEFT JOIN / RIGHT JOIN
☑ FULL OUTER JOIN
☑ Practice combining data from multiple tables
📊 4. Aggregation & Grouping
☑ COUNT, SUM, AVG, MIN, MAX
☑ GROUP BY & HAVING
☑ Aggregate filtering
📈 5. Subqueries & CTEs
☑ Use subqueries inside SELECT/WHERE
☑ WITH clause for common table expressions
☑ Nested queries and optimization basics
🧮 6. Window Functions
☑ RANK(), ROW_NUMBER(), DENSE_RANK()
☑ PARTITION BY & ORDER BY
☑ LEAD(), LAG(), SUM() OVER
🧹 7. Data Cleaning with SQL
☑ Remove duplicates (DISTINCT, ROW_NUMBER)
☑ Handle NULLs
☑ Use CASE WHEN for conditional logic
🛠️ 8. Practice & Real Tasks
☑ Write queries from real datasets
☑ Analyze sales, customers, transactions
☑ Build reports with JOINs and aggregations
📁 9. Tools to Use
☑ PostgreSQL / MySQL / SQL Server
☑ db-fiddle, Mode Analytics, DataCamp, StrataScratch
☑ VS Code + SQL extensions
🚀 10. Interview Prep
☑ Practice 50+ SQL questions
☑ Solve problems on LeetCode, HackerRank
☑ Explain query logic clearly in mock interviews
💬 Tap ❤️ if this was helpful! | 574 |
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